Objective:To explore the relationship between the lesions of white matter in magnetic resonance imaging(MRI)and the cognitive impairment in patients with depression.Methods:A total of 80 patients with depression who admitted to emergency general hospital from January 2022 to January 2024 were retrospectively collected.According to whether there were lesions of brain white matter,they were divided into study group(38 cases with lesions)and control group(42 cases without lesions).Results:There were significant differences in age,years of receiving education,and total course of disease between study group and control group(t=2.84,2.62,2.19,P<0.05).The scores of attention and computational ability,memory,naming,attention/concentration,language and visual spatial skills,and total score of MoCA score were respectively(2.56±0.50,2.35±0.30,3.22±1.30,2.12±0.59,1.17±0.35,1.10±0.30,1.35±0.35,13.87±1.81)in study group,all of which were significantly lower than those(3.10±0.55,2.60±0.35,4.01±1.65,2.60±0.58,1.40±0.55,1.40±0.84,1.80±0.79,16.91±2.10)in control group,and the differences of them between two groups were statistically significant(t=4.578,3.412,2.361,3.666,2.205,2.084,3.234,6.900,P<0.05).There were correlations between the lesion of brain white in MRI and the Montreal cognitive assessment(MoCA)score,between that and age,between that and years of receiving education,between that and total course of disease,and between that and Hamilton depression rating scale(HAMD)score in patients with depression(OR=2.19,0.93,1.11,0.89,1.31,P<0.05).The results of Logistic regression analysis showed that MoCA score and lesions of brain white matter in MRI had independent effects on cognitive impairment of patients with depression.Conclusion:The lesion of brain white matter is an important factor for cognitive impairment in patients with depression.

Objective:To investigate the combined therapeutic role of the AKT inhibitor MK2206 and Ruxolitinib in treating Myeloproliferative Neoplasms (MPN) driven by a calreticulin (CALR) gene mutation.Methods:① Murine bone marrow c-kit + cells were isolated by sacrificing mice and harvesting bone marrow from the femur, tibia, and ilium for subsequent c-kit + cell sorting. ② A CALR transplantation mouse model was established. GFP-tagged retroviral vectors containing either the CALR gene mutation or the migR1 control were constructed, packaged in Platinum-E cells, and used to transduce murine bone marrow c-kit + cells. These transduced cells were then transplanted into lethally irradiated female recipient mice via tail vein injection. ③ Following successful engraftment, the mice were randomly assigned to four treatment groups for intragastric administration. Complete blood counts were monitored periodically, and the spleen size and weight of transplanted mice were measured. ④ Flow cytometry was used to quantify the proportions of GFP + tumor cells, megakaryocytic lineage cells, and hematopoietic stem cells in both splenic and bone marrow tissues. Histopathological examination was performed to evaluate the degree of tumor cell infiltration in these organs. Results:① Following gavage treatment, peripheral blood platelet (PLT) and white blood cell counts were significantly lower in the combined AKT inhibitor MK2206 and Ruxolitinib group compared to the MK2206, Ruxolitinib, and control groups ( P<0.05). ② In comparison with the MK2206 and Ruxolitinib monotherapy groups, the combination therapy group exhibited a significant reduction in spleen weight and a marked improvement in splenomegaly at 30 weeks post-transplantation ( P<0.05). ③ After four weeks of continuous treatment, combined administration resulted in a significant decrease in the proportion of megakaryocytic lineage cells and GFP + tumor cells in the bone marrow and spleen ( P<0.05). Additionally, the proportion of hematopoietic stem cells in the bone marrow was also significantly reduced ( P<0.05). ④ Histopathological analysis (H&E staining) of bone marrow and spleen tissues confirmed that the combined regimen decreased both tumor cell infiltration and the proportion of abnormal megakaryocytes in these organs. Conclusion:The combination of AKT inhibitor MK2206 and Ruxolitinib is effective at significantly ameliorating disease symptoms and reducing tumor infiltration in vivo in mice with a myeloproliferative tumor transplantation driven by a CALR gene mutation.

Objective To construct a model for predicting the risk of essential hypertension accompanied by left ventricular hypertrophy using machine learning algorithms based on pulse diagram parameters;To explore its clinical application value.Methods A total of 295 patients with essential hypertension who were hospitalized in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai Hospital of Traditional Chinese Medicine and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine were selected from July 2020 to May 2021 and July 2023 to July 2024.According to the echocardiographic results,the selected research subjects were divided into the essential hypertension with left ventricular hypertrophy group(referred to as the"LVH group")and the essential hypertension without left ventricular hypertrophy group(referred to as the"non-LVH group").The general data and clinical biochemical indicators were collected,and the pulse diagram parameters of the patients were detected using the SMART-I type TCM digital pulse analyzer.A clinical prediction model was constructed based on decision tree,support vector machine and extreme gradient boosting model algorithms.The predictive performance of the model was evaluated in terms of discrimination,calibration and clinical prediction ability by using the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis respectively.The influence of each predictive factor on the risk of LVH in essential hypertension was explained based on the SHAP algorithm.Results Compared with the non-LVH group,the BMI,the proportion of males,drinkers and smokers was lower in the LVH group,with statistical significance(P<0.05);the thickened ventricular wall,left ventricular internal dimension enlargement,left common carotid artery intima-media thickness and high density lipoprotein cholesterol were higher in the LVH group than in the non-LVH group(P<0.05);the left common carotid peak systolic velocity,left common carotid resistance index,serum uric acid and serum creatinine were lower in the LVH group than in the non-LVH group(P<0.05).The pulse diagram parameters T4,T,W1,W2,H3/H1 and H4/H1 were higher in the LVH group than in the non-LVH group(P<0.05).The areas of the ROC curves of the models constructed by the three types of machine learning algorithms were 0.887,0.962 and 0.873 respectively,indicating that the model had good discrimination and certain diagnostic efficacy.The calibration curve suggested that the prediction accuracy of the model was average;the clinical decision curve showed that XGBoost model has a higher net benefit.Conclusion The interpretable model constructed based on pulse diagram parameters and machine learning algorithms can be used as a reliable tool for predicting the risk of essential hypertension with LVH.

Objective To construct a model for predicting the risk of essential hypertension accompanied by left ventricular hypertrophy using machine learning algorithms based on pulse diagram parameters;To explore its clinical application value.Methods A total of 295 patients with essential hypertension who were hospitalized in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai Hospital of Traditional Chinese Medicine and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine were selected from July 2020 to May 2021 and July 2023 to July 2024.According to the echocardiographic results,the selected research subjects were divided into the essential hypertension with left ventricular hypertrophy group(referred to as the"LVH group")and the essential hypertension without left ventricular hypertrophy group(referred to as the"non-LVH group").The general data and clinical biochemical indicators were collected,and the pulse diagram parameters of the patients were detected using the SMART-I type TCM digital pulse analyzer.A clinical prediction model was constructed based on decision tree,support vector machine and extreme gradient boosting model algorithms.The predictive performance of the model was evaluated in terms of discrimination,calibration and clinical prediction ability by using the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis respectively.The influence of each predictive factor on the risk of LVH in essential hypertension was explained based on the SHAP algorithm.Results Compared with the non-LVH group,the BMI,the proportion of males,drinkers and smokers was lower in the LVH group,with statistical significance(P<0.05);the thickened ventricular wall,left ventricular internal dimension enlargement,left common carotid artery intima-media thickness and high density lipoprotein cholesterol were higher in the LVH group than in the non-LVH group(P<0.05);the left common carotid peak systolic velocity,left common carotid resistance index,serum uric acid and serum creatinine were lower in the LVH group than in the non-LVH group(P<0.05).The pulse diagram parameters T4,T,W1,W2,H3/H1 and H4/H1 were higher in the LVH group than in the non-LVH group(P<0.05).The areas of the ROC curves of the models constructed by the three types of machine learning algorithms were 0.887,0.962 and 0.873 respectively,indicating that the model had good discrimination and certain diagnostic efficacy.The calibration curve suggested that the prediction accuracy of the model was average;the clinical decision curve showed that XGBoost model has a higher net benefit.Conclusion The interpretable model constructed based on pulse diagram parameters and machine learning algorithms can be used as a reliable tool for predicting the risk of essential hypertension with LVH.

Objective:To explore the relationship between the lesions of white matter in magnetic resonance imaging(MRI)and the cognitive impairment in patients with depression.Methods:A total of 80 patients with depression who admitted to emergency general hospital from January 2022 to January 2024 were retrospectively collected.According to whether there were lesions of brain white matter,they were divided into study group(38 cases with lesions)and control group(42 cases without lesions).Results:There were significant differences in age,years of receiving education,and total course of disease between study group and control group(t=2.84,2.62,2.19,P<0.05).The scores of attention and computational ability,memory,naming,attention/concentration,language and visual spatial skills,and total score of MoCA score were respectively(2.56±0.50,2.35±0.30,3.22±1.30,2.12±0.59,1.17±0.35,1.10±0.30,1.35±0.35,13.87±1.81)in study group,all of which were significantly lower than those(3.10±0.55,2.60±0.35,4.01±1.65,2.60±0.58,1.40±0.55,1.40±0.84,1.80±0.79,16.91±2.10)in control group,and the differences of them between two groups were statistically significant(t=4.578,3.412,2.361,3.666,2.205,2.084,3.234,6.900,P<0.05).There were correlations between the lesion of brain white in MRI and the Montreal cognitive assessment(MoCA)score,between that and age,between that and years of receiving education,between that and total course of disease,and between that and Hamilton depression rating scale(HAMD)score in patients with depression(OR=2.19,0.93,1.11,0.89,1.31,P<0.05).The results of Logistic regression analysis showed that MoCA score and lesions of brain white matter in MRI had independent effects on cognitive impairment of patients with depression.Conclusion:The lesion of brain white matter is an important factor for cognitive impairment in patients with depression.

Objective:To investigate the combined therapeutic role of the AKT inhibitor MK2206 and Ruxolitinib in treating Myeloproliferative Neoplasms (MPN) driven by a calreticulin (CALR) gene mutation.Methods:① Murine bone marrow c-kit + cells were isolated by sacrificing mice and harvesting bone marrow from the femur, tibia, and ilium for subsequent c-kit + cell sorting. ② A CALR transplantation mouse model was established. GFP-tagged retroviral vectors containing either the CALR gene mutation or the migR1 control were constructed, packaged in Platinum-E cells, and used to transduce murine bone marrow c-kit + cells. These transduced cells were then transplanted into lethally irradiated female recipient mice via tail vein injection. ③ Following successful engraftment, the mice were randomly assigned to four treatment groups for intragastric administration. Complete blood counts were monitored periodically, and the spleen size and weight of transplanted mice were measured. ④ Flow cytometry was used to quantify the proportions of GFP + tumor cells, megakaryocytic lineage cells, and hematopoietic stem cells in both splenic and bone marrow tissues. Histopathological examination was performed to evaluate the degree of tumor cell infiltration in these organs. Results:① Following gavage treatment, peripheral blood platelet (PLT) and white blood cell counts were significantly lower in the combined AKT inhibitor MK2206 and Ruxolitinib group compared to the MK2206, Ruxolitinib, and control groups ( P<0.05). ② In comparison with the MK2206 and Ruxolitinib monotherapy groups, the combination therapy group exhibited a significant reduction in spleen weight and a marked improvement in splenomegaly at 30 weeks post-transplantation ( P<0.05). ③ After four weeks of continuous treatment, combined administration resulted in a significant decrease in the proportion of megakaryocytic lineage cells and GFP + tumor cells in the bone marrow and spleen ( P<0.05). Additionally, the proportion of hematopoietic stem cells in the bone marrow was also significantly reduced ( P<0.05). ④ Histopathological analysis (H&E staining) of bone marrow and spleen tissues confirmed that the combined regimen decreased both tumor cell infiltration and the proportion of abnormal megakaryocytes in these organs. Conclusion:The combination of AKT inhibitor MK2206 and Ruxolitinib is effective at significantly ameliorating disease symptoms and reducing tumor infiltration in vivo in mice with a myeloproliferative tumor transplantation driven by a CALR gene mutation.

Objective:To develop an " AI-Doctor" collaborative intelligent model for precise segmentation of retinal non-perfusion areas and evaluate its effectiveness.Methods:Seventy-three retinal non-perfusion images were collected from diabetic retinopathy patients who visited Xuzhou Medical University Affiliated Xuzhou Municipal Hospital and underwent the ultra-widefield fluorescein angiography (UWFA) from December 2022 to January 2024.These images were divided into a training set of 38 images, a validation set of 10 images, and a test set of 25 images.A VGG-UNet model was created, which is an optimization of the combination of VGG-16 and U-Net.Large-scale and small-scale training datasets were created from the UWFA images, and the VGG-UNet was trained on each to obtain corresponding large-scale and small-scale networks.Initial segmentation of non-perfusion areas in UWFA images was conducted using the large-scale network.A physician interaction module was introduced to enhance local segmentation accuracy via the small-scale network, allowing for precise segmentation of non-perfusion areas in UWFA images.The efficacy of the " AI-Doctor" collaborative model was then compared with that of traditional physician annotation methods.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Xuzhou Medical University Affiliated Xuzhou Municipal Hospital (No.xyy11[2023]069).Written informed consent was obtained from each subject.Results:The VGG-UNet model was generally able to accurately segment retinal non-perfusion areas.However, problems such as missegmentation, omission, and imprecision were observed at the edge of the eyeball.After the introduction of the physician interaction module, the average segmentation accuracy was improved to 90.36%, showing a significant improvement over conventional methods.Based on the VGG-UNet, a collaborative intelligent segmentation model of " AI-Doctor" was constructed, which can accurately segment images of the non-perfusion area of retinal blood vessels.The validation results showed that the average time of " AI-Doctor" collaborative annotation was about 3.0 minutes, which was significantly shorter than the 29.6 minutes of the traditional annotation method, and the efficiency was improved by about 10 times, and the segmentation accuracy reached 90.36%.Conclusions:An intelligent segmentation model with " AI-Doctor" collaboration is successfully established to achieve efficient and accurate segmentation of the non-perfused area of retinal blood vessels.

Objective To investigate the effect of Alogliptin benzoate on the serum autophagy markers in type 2 diabetes mellitus(T2DM)patients.Methods Eighty newly diagnosed T2DM patients who visited the Department of Endocrinology in Baoding No.1 Central Hospital from December 2021 to October 2022 were randomly divided into a group treated with Metformin(Met group,n=40)and a group treated with Met and Alog(Met+Alog group,n=40).The differences in BMI,WHR,FPG,HbA1c,Atg7 and Beclin-1 between two groups before and after 12 weeks of treatment were compared.Results After treatment,the levels of Atg7 and Beclin-1 increased in both groups(P<0.05),while FPG,HbA1c and HOMA-IR decreased(P<0.05).After treatment,Atg7,Beclin-1 and HDL-C in Met+Alog group were higher than those in Met group(P<0.05).Pearson correlation analysis showed that Atg7 was negatively correlated with BMI,FPG and HbA1c(P<0.05);Beclin-1 was positively correlated with HDL-C(P<0.05),and negatively correlated with BMI,FPG,HbA1c,and TG(P<0.05).Meta linear regression analysis showed that BMI was the influencing factor of Atg7,while BMI and HDL-C were the influencing factors of Beclin-1.Conclusion Alogliptin benzoate may improve islet β cell function by up-regulating the expression of autophagy related factors Atg7 and Beclin-1 in patients with T2DM.