Objective To analyse and explore whether there is a causal association without confounding factors between malignant haematological diseases and frailty based on a bidirectional Mendelian randomisation(MR)analysis,and to provide a theoretical basis for clinical management of the frailty associated with malignant haematological diseases.Methods In December 2023,the IEU OpenGWAS database(https://gwas.mrcieu.ac.uk/)was searched to acquire the datasets in genome-wide association studies(GWAS)derived from non-overlapping multi-ethnic populations based on Mendelian Randomisation(MR)analysis.The bidirectional causal association was verified utilising the two-sample MR approach.Single nucleotide polymorphisms(SNPs)of the frailty index(FI)(n=175,226),haematological malignancies(n=212,453),multiple myeloma/malignant plasmacytoma(n=218,792),and follicular lymphoma(n=181,278)were used as the study instruments.Results The analysis with the statistic inverse variance weighted method(IVW)showed that haematological malignancies(OR=1.00,95%CI:0.98-1.00,P=0.797),multiple myeloma/malignant plasma cell tumours(OR=1.00,95%CI 0.99～1.01,P=0.982),and follicular lymphoma(OR=1.00,95%CI:0.99～1.01,P=0.314)were not causally associated with genetically predicted FI.Similarly,FI was not significantly or causally correlated with haematological malignancies(OR=0.89,95%CI:0.25～3.12,P=0.861),multiple myeloma/malignant plasma cell tumours(OR=0.52,95%CI:0.00～3.13,P=0.473),and follicular lymphoma(OR=1.06,95%CI:0.00～5.19,P=0.944).Conclusion No causal relationship between the malignant haematological diseases and frailty was found in this study.It suggests that other factors might exist to cause the malignant haematological frailty.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Objective To analyse and explore whether there is a causal association without confounding factors between malignant haematological diseases and frailty based on a bidirectional Mendelian randomisation(MR)analysis,and to provide a theoretical basis for clinical management of the frailty associated with malignant haematological diseases.Methods In December 2023,the IEU OpenGWAS database(https://gwas.mrcieu.ac.uk/)was searched to acquire the datasets in genome-wide association studies(GWAS)derived from non-overlapping multi-ethnic populations based on Mendelian Randomisation(MR)analysis.The bidirectional causal association was verified utilising the two-sample MR approach.Single nucleotide polymorphisms(SNPs)of the frailty index(FI)(n=175,226),haematological malignancies(n=212,453),multiple myeloma/malignant plasmacytoma(n=218,792),and follicular lymphoma(n=181,278)were used as the study instruments.Results The analysis with the statistic inverse variance weighted method(IVW)showed that haematological malignancies(OR=1.00,95%CI:0.98-1.00,P=0.797),multiple myeloma/malignant plasma cell tumours(OR=1.00,95%CI 0.99～1.01,P=0.982),and follicular lymphoma(OR=1.00,95%CI:0.99～1.01,P=0.314)were not causally associated with genetically predicted FI.Similarly,FI was not significantly or causally correlated with haematological malignancies(OR=0.89,95%CI:0.25～3.12,P=0.861),multiple myeloma/malignant plasma cell tumours(OR=0.52,95%CI:0.00～3.13,P=0.473),and follicular lymphoma(OR=1.06,95%CI:0.00～5.19,P=0.944).Conclusion No causal relationship between the malignant haematological diseases and frailty was found in this study.It suggests that other factors might exist to cause the malignant haematological frailty.

Background: In the field of neuroscience research, executive functions (EFs) are of great significance. Patients with Parkinson’s disease (PD) often encounter the problem of EFs impairment, which severely affects their lives and health.However, currently, the methods for evaluating EFs in experimental mice are limited, and there is a lack of effective evaluation indicators for touchscreen behavioral tests in different disease model mice. This study aims to establish a paradigm process and an evaluation baseline for touchscreen behavioral analysis in PD model mice, deeply explore the mechanisms of EFs impairment in PD, and provide a crucial foundation for subsequent research and treatment. Methods: Thirty clean - grade SNCA*A53T transgenic mice and forty clean - grade C57BL/6J wild - type mice were selected. For A53T mice, genetic identification was carried out. Molecular biology techniques such as PCR were used to determine their genetic characteristics. Protein detection was conducted through methods like Western blot to clarify the expression of relevant proteins. In terms of behavioral tests, the five - choice serial reaction time task (5 - CSRT) was adopted, and various behavioral data of mice in this task were recorded. Four groups were set up: the control group, the MPTP group. Different groups of mice were given specific treatments. For example, the MPTP group of mice was injected with MPTP to construct a drug - induced PD model. Principal component analysis (PCA) and receiver operating characteristic (ROC) curves were used to analyze the obtained touchscreen behavioral baseline indicators of mice, and key indicators were screened and evaluated. Results: In the 5 - CSRT, the optimal stage for wild - type C57 mice to achieve an accuracy rate of ≥ 80% was from the 11th to the 13th cycle, while for A53T mice, it was the 11th cycle. The EFs of A53T mice were impaired, with abnormalities in the accuracy rate, trace number, and number of punished times in the 5 - CSRT. When identifying drug - induced PD models, the 13th cycle of the 5 - CSRT was more effective; when identifying transgenic PD models, the 11th cycle was more suitable. Interventions could be carried out after the baseline accuracy rate reached 80%. Through the “genetic - environmental” dual - axis drive, the “chronic - acute” time - dimension complementarity, and the “mechanism - transformation” multi - level verification, the A53T and MPTP dual models were used to comprehensively cover the pathological cycle of PD EFs impairment. Conclusions This study has successfully established a paradigm process and an evaluation baseline for touchscreen behavioral analysis in PD model mice. This provides an important basis for a deep understanding of the mechanisms of EFs impairment in PD patients, has potential guiding significance for the development of intervention strategies and treatment methods for EFs impairment in PD patients, and is expected to promote new progress in PD research in the field of neuroscience.

Purpose To investigate the abnormal brain development in fetuses with isolated mild to moderate ventriculomegaly(VM)during the second and third trimesters of pregnancy by using semi-quantitative fetal total maturation score(fTMS).Materials and Methods A retrospective analysis was performed on 45 normal fetuses and 78 abnormal fetuses who underwent fetal MRI in the General Hospital of Ningxia Medical University from January 2018 to October 2022.fTMS was used to score all images.Linear regression was used to assess the relationship between fTMS and gestational age between normal and abnormal fetuses and to analyze the differences between fetuses with isolated mild and moderate VM and controls.Results In the control group,fetal fTMS was significantly positively correlated with gestational age(r=0.939,P<0.05).The linear regression equation between fTMS(Y)and gestational age(X)was as follows:Y=-28.1+1.25X.In the mild and moderate isolated VM groups,fTMS was positively correlated with gestational age(r=0.945,0.906,P<0.05).The linear regression equations of fTMS(Y)and gestational age(X)were:Y=-28.46+1.24X,Y=-25.57+1.13X.The average fTMS of healthy fetus,mild VM and moderate VM were(10.55±4.25)points,(10.13+4.08)points and(9.22±3.77)points,respectively.There was no significant difference in fTMS between the 49 fetuses with mild isolated VM and the control group(t=1.651,P>0.05).There was significant difference in fTMS between the 29 fetuses with moderate isolated VM and the control group(t=2.306,P<0.05).Conclusion fTMS is suitable for routine clinical use and sensitive to differences in brain maturation between fetuses with isolated moderate VM and healthy controls.

Human myelination begins in the fifth month of fetal development and continues after birth.Myelin development plays a key role in establishing and maintaining information conduction,coordination and communication within the brain,so prenatal quantitative assessment of myelin development is important.In recent years,many MRI techniques for myelin imaging have been developed and implemented,and quantitative MRI assessment of fetal myelin development has received increasing attention.In this review,we discuss the known structural and functional changes in the development of the myelin sheath of the fetal central nervous system,and review the research progress and future expectations of quantitative fetal MRI imaging.

Objective To establish an ideal modeling method for diarrhea predominant irritable bowel syndrome(IBS-D)with anxiely and depression in rats,and to provide a basis for the clinical study of IBS-D.Methods 60 rats were used in this study.(1)At first,20 rats were randomly divided into blank,3%acetic acid enema,4%acetic acid enema,and 5%acetic acid enema groups.After the modeling and observation period,the diarrhea status and the degree of colon injury caused by different modeling concentrations were observed by diarrhea related index and colon histopathology.(2)After the optimal modeling concentration was assessed,40 rats were randomly divided into control(a),acetic acid enema(b),acetic acid+binding(c),and acetic acid+binding+tail clip(d)groups and correspondingly treated for 8 days.After the treatments,the general condition,diarrhea-related index,open field test(OFT)score,and colonic histopathology of rats were evaluated.Results(1)Compared with the blank group,the fecal trait score of 4%acetic acid enema group was increased on days 1 to 3 after intervention(P＜0.001),and gradually decreased on days 4 to 7 after intervention.After 1 week,there was no significant difference between the fecal trait score and that of the blank group(P＞0.05).Body weight was lower(P＜0.01),fecal water content was higher(P＜0.001).Compared with blank group,body weight of the 5%acetic acid enema group was decreased(P＜0.001),the fecal trait score and diarrhea index were increased(P＜0.01).No significant difference was found between 3%acetic acid enema and blank groups.The pathological colon tissue showed that,compared with the blank group,the mucosal structure of the 4%acetic acid enema group was complete with a small amount of inflammatory cell infiltration,and the pathological tissue score showed no significant difference(P＞0.05),whereas the 5%acetic acid enema had a medium to large amount of inflammatory cell infiltration,and the pathological tissue score was increased(P＜0.01).(2)Compared with group a,group b had lower body weight(P＜0.001),and higher fecal trait score,fecal water content and diarrhea index(P＜0.01).Compared with a and b groups,the body weight of c and d groups was lower(P＜0.001),the fecal traits score,fecal water content,and diarrhea index were increased(P＜0.01),and the colon running time was decreased(P＜0.01).Compared with group c,Fecal water content in group D was higher(P＜0.001).In the OFT score,compared with a and b groups,the OFT distance,standing times,and upright times in c and d groups were lower(P＜0.05).Compared with c,the OFT distance,standing times,and upright times in d group were lower(P＜0.05).The pathological tissue of colon showed that the mucosal structure of the four groups was complete,and there were different degrees of inflammatory cell infiltration.The pathological tissue scores of groups c and d were higher than those of groups a and b(P＜0.05).Conclusions The 4%acetic acid concentration is appropriate for IBS-D modeling.After superposition and binding,the IBS-D diarrhea and internal hypersensitivity characteristic state can be better simulated.After superposition of a tail clip,the IBS-D model of liver stagnation and spleen deficiency can be established successfully.

Purpose To explore the predictive value of nomogram model for invasive breast cancer with axillary lymph node metastasis.Materials and Methods Retrospective analysis was made on 122 patients suspected to be breast cancer in the General Hospital of Ningxia Medical University from September 2020 to March 2022.According to whether there was axillary lymph node metastasis,all subjects were divided into 57 patients in the metastasis group and 65 patients in the non-metastasis group.All lesions were pathologically confirmed by surgery.The two groups received synthesis of magnetic resonance imaging(syMRI),dynamic contrast enhancement magnetic resonance imaging(DCE-MRI)and diffusion weighted imaging(DWI)scans.The syMRI parameters[including T1,T2,proton density(PD)],DCE-MRI time signal intensity curve,apparent diffusion coefficient(ADC)value of breast lesions were measured.Compared the difference of parameters between the two groups,and screened the independent risk factors of invasive breast cancer with axillary lymph node metastasis.Results Logistic regression results showed that Ki-67(OR=2.971,95%CI 1.306-6.762,P=0.009),lesion size(OR=1.652,95%CI 1.067-2.556,P=0.024),ADCratio(OR=1.685,95%CI 1.014-2.801,P=0.044),T2ratio(OR=3.015,95%CI 1.433-6.340,P=0.003),PDratio(OR=2.782,95%CI 1.471-5.262,P=0.002)were independent risk factors for invasive breast cancer with axillary lymph node metastasis.The comparison of the five models showed that the Logistic regression model had the best performance,with the area under curve of 0.729(95%CI 0.621-0.789),the accuracy,specificity and sensitivity were 70.65%,62.79%and 77.55%,respectively.The accuracy of the nomogram model was tested,and C-index=0.844,the accuracy of the nomogram model established was good,cut-off risk was 0.468,and the cut-off score was 143.50,which means that when the total score exceeds 143.50,the risk of axillary lymph node metastasis would be higher than 46.8%.Conclusion Nomogram model has a good predictive ability for invasive breast cancer patients with axillary lymph node metastasis.

The in vitro release is an important index to evaluate the quality of liposome formulation.Currently, there is no evaluation method for the in vitro release of liposome in pharmacopoeia of various countries, which leads to the lack of unified standard and safety guarantee for the quality evaluation of liposome formulation.Taking the self-made paclitaxel derivative liposomes as an example, the paddle membrane binding method established by optimizing external release conditions was used to simulate the complete release of paclitaxel derivative drugs in 12 hours under physiological conditions.The results showed that using 0.5% SDS-HEPES as the release medium and a dialysis bag with a molecular weight cutoff of 1 000 kD to release the liposome solution met the requirements and had discrimination ability, providing a reference for the development of drug-loaded liposomes release methods in vitro.