In recent years, 7-tesla high-resolution magnetic resonance imaging (7 T HR-MRI) has demonstrated significant advantages in evaluating intracranial atherosclerotic plaques due to its ultrahigh signal-to-noise ratio and submillimeter isotropic spatial resolution. This cutting-edge technique enables precise visualization of plaque microstructures, including morphological characteristics and compositional features. Moreover, it allows noninvasive in vivo imaging of small intracranial vessels and provides clear delineation of the anatomical relationship between perforating artery ostia and their parent arteries. Although not yet widely adopted in clinical practice, 7 T HR-MRI shows promising potential for plaque vulnerability assessment, stroke mechanism elucidation, and therapeutic monitoring. This review summarizes recent advancements in 7 T MRI applications for intracranial atherosclerotic disease, aiming to provide novel insights for precision medicine in clinical practice.

Objective:To investigate whether deltoid ligament (DL) injury produces axial-plane rotational instability of the ankle in patients with chronic ankle instability (CAI).Methods:A retrospective study was conducted to analyze the 33 patients with CAI who had been treated at Department of Orthopaedics, The First Affiliated Hospital of Nanjing Medical University between January 2023 and December 2024. The cohort consisted of 17 males and 16 females with an age of (31.5±9.9) years. The patients were assigned into 2 groups based on the presence of DL injury: a lateral chronic ankle instability (LCAI) group ( n=17) and a rotational ankle instability (RAI) group ( n=16). Barefoot natural walking trials were performed in all patients. Three-dimensional kinematic data were synchronously collected using an optical motion capture system (12 cameras) and force plates. A lower extremity model was constructed to obtain shank axial rotation (internal/external rotation) and rear-foot inversion/eversion angles. Continuous relative phase (CRP) analysis was employed to assess shank-rearfoot movement coupling. The mean absolute relative phase (MARP) and deviation phase (DP) were calculated. Results:There was no statistically significant difference in the clinical baseline data between the 2 groups, indicating comparability ( P>0.05). Throughout the gait cycle, no significant differences were found in shank rotation angles or rear-foot eversion angles between the RAI group and the LCAI group. However, CRP analysis revealed that during the early stance phase (initial contact and loading response), shank-rearfoot coupling was significantly lower in the RAI group than in the LCAI group. In the early stance phase, the CRP values in the RAI group were significantly higher than those in the LCAI group. The CRP curve changes in the RAI group were consistently higher in the standce phase of the entire gait cycle than those in the LCAI group, and the peak value of the CRP curve was larger in the RAI group. Concurrently, the RAI group exhibited significantly higher MARP and DP values than the LCAI group (27.48°±14.54° versus 15.21°±9.56°; 26.02°±11.73° versus 17.83°±9.82°) (both P<0.05). Conclusion:DL injury significantly damages the axial rotational stability of the ankle joint and significantly exacerbates the mechanical instability of the ankle joint in CAI patients.

Objective:To evaluate the association between postoperative delirium (POD) and preoperative frailty in elderly patients undergoing knee or hip arthroplasty.Methods:This nested case-control study utilized medical records from elderly patients who underwent knee or hip arthroplasty under combined spinal-epidural anesthesia at Qingdao Municipal Hospital between September 2021 and May 2023. Participants were divided into 2 groups based on clinically diagnosed POD: POD group ( n=53) and non-POD group ( n=256). Univariate analysis was conducted on suspected influencing factors, and logistic regression analysis was utilized to identify the risk factors for POD. Receiver operating characteristic and clinical decision curves were plotted to evaluate the predictive performance of these risk factors for POD. Mediation analysis was performed, and a clinically applicable nomogram was constructed to achieve visual prediction of outcomes. Results:There were statistically significant differences in age, preoperative frailty, body mass index, American Society of Anesthesiologists Physical Status classification, Memorial Delirium Assessment Scale scores, and concentrations of Aβ 42, Aβ 40, phosphorylated tau protein (p-tau protein) and tau protein, Aβ 42/tau ratio and Aβ 42/p-tau ratio in cerebrospinal fluid (CSF) between non-POD group and POD group ( P<0.05). Preoperative frailty was a risk factor for POD ( P<0.05). Mediation analysis revealed that the association between preoperative frailty and POD was mediated by CSF tau protein concentrations. The area under the receiver operating characteristic curve of preoperative frailty and CSF biomarker concentrations in predicting POD was 0.974 ( P<0.05). The clinical decision curve demonstrated that the model combining the preoperative frailty and CSF biomarker concentrations predicted a higher net benefit ( P<0.05). The clinical decision curve showed that the model combining preoperative frailty and CSF biomarker concentrations predicted a higher net benefit. Conclusions:Preoperative frailty is a risk factor for POD in elderly patients undergoing knee or hip arthroplasty, and its combination with CSF biomarker concentrations can effectively predict the occurrence of POD. CSF tau concentration mediates the association between preoperative frailty and development of POD.

Objective To observe the value of intravoxel incoherent motion(IVIM)histogram parameters for predicting perineural invasion(PNI)of rectal cancer.Methods Fifty-five patients with rectal cancer were retrospectively enrolled and divided into positive group(n=23)and negative group(n=32)according to PNI or not.Histogram parameters of apparent diffusion coefficient(ADC),perfusion fraction(f),true diffusion coefficient(D)and pseudo diffusion coefficient(D*)were obtained based on MR IVIM images,including the mean,skewness,kurtosis,entropy,as well as the 25th,50th and 75th percentiles(25th,50th,75th).The above IVIM histogram parameters were compared between groups,and those with significant difference were included in binary stepwise logistic regression analysis to screen independent factors for predicting PNI status of rectal cancer.Then a combined parameter model was established.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of IVIM histogram parameters and combined parameter model for predicting PNI of rectal cancer.Results ADCmean,ADC25th,ADC50th,ADC75th,fmean,f25th,f50th,f75th and Dentropy in positive group were all lower than those in negative group(all P＜0.05).AUC of the above parameters for predicting PNI of rectal cancer was 0.693,0.665,0.701,0.675,0.831,0.847,0.835,0.722 and 0.785,respectively.The sensitivity,specificity and AUC of combined parameter model based on f25th and Dentropy for predicting PNI of rectal cancer was 95.65％,65.63％and 0.897,respectively.Conclusion IVIM histogram parameters could be used to predict PNI of rectal cancer effectively.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

With the increasingly stringent requirements of international standards for the stability of clinical samples,there has been a significant rise in clinical laboratories'demand for sample stability research.To ensure the accuracy of laboratory test results,conducting research and verification work related to sample stability has become particularly urgent.However,there is currently a lack of unified standards for clinical sample stability studies both domestically and internationally.In order to better promote the resach and validation of sample stability in clinical laboratroy projects,this review summarized the latest progress in sample stability research methods by reviewing relevant literature and guidelines.It recommends referencing guidance from international organizations in clinical sample stability studies,such as the cress checklist for reporting stability studies and the"recommendations for the design of stability studies for clinical samples"issued by the European Federation of Clinical Chemistry and Laboratory Medicine(EFLM).Laboratories should select either continuous experimental design or discrete experimental design based on their practical circumstances when conducting such research.

With the increasingly stringent requirements of international standards for the stability of clinical samples,there has been a significant rise in clinical laboratories'demand for sample stability research.To ensure the accuracy of laboratory test results,conducting research and verification work related to sample stability has become particularly urgent.However,there is currently a lack of unified standards for clinical sample stability studies both domestically and internationally.In order to better promote the resach and validation of sample stability in clinical laboratroy projects,this review summarized the latest progress in sample stability research methods by reviewing relevant literature and guidelines.It recommends referencing guidance from international organizations in clinical sample stability studies,such as the cress checklist for reporting stability studies and the"recommendations for the design of stability studies for clinical samples"issued by the European Federation of Clinical Chemistry and Laboratory Medicine(EFLM).Laboratories should select either continuous experimental design or discrete experimental design based on their practical circumstances when conducting such research.

Objective:To evaluate the association between postoperative delirium (POD) and preoperative frailty in elderly patients undergoing knee or hip arthroplasty.Methods:This nested case-control study utilized medical records from elderly patients who underwent knee or hip arthroplasty under combined spinal-epidural anesthesia at Qingdao Municipal Hospital between September 2021 and May 2023. Participants were divided into 2 groups based on clinically diagnosed POD: POD group ( n=53) and non-POD group ( n=256). Univariate analysis was conducted on suspected influencing factors, and logistic regression analysis was utilized to identify the risk factors for POD. Receiver operating characteristic and clinical decision curves were plotted to evaluate the predictive performance of these risk factors for POD. Mediation analysis was performed, and a clinically applicable nomogram was constructed to achieve visual prediction of outcomes. Results:There were statistically significant differences in age, preoperative frailty, body mass index, American Society of Anesthesiologists Physical Status classification, Memorial Delirium Assessment Scale scores, and concentrations of Aβ 42, Aβ 40, phosphorylated tau protein (p-tau protein) and tau protein, Aβ 42/tau ratio and Aβ 42/p-tau ratio in cerebrospinal fluid (CSF) between non-POD group and POD group ( P<0.05). Preoperative frailty was a risk factor for POD ( P<0.05). Mediation analysis revealed that the association between preoperative frailty and POD was mediated by CSF tau protein concentrations. The area under the receiver operating characteristic curve of preoperative frailty and CSF biomarker concentrations in predicting POD was 0.974 ( P<0.05). The clinical decision curve demonstrated that the model combining the preoperative frailty and CSF biomarker concentrations predicted a higher net benefit ( P<0.05). The clinical decision curve showed that the model combining preoperative frailty and CSF biomarker concentrations predicted a higher net benefit. Conclusions:Preoperative frailty is a risk factor for POD in elderly patients undergoing knee or hip arthroplasty, and its combination with CSF biomarker concentrations can effectively predict the occurrence of POD. CSF tau concentration mediates the association between preoperative frailty and development of POD.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective To observe the value of intravoxel incoherent motion(IVIM)histogram parameters for predicting perineural invasion(PNI)of rectal cancer.Methods Fifty-five patients with rectal cancer were retrospectively enrolled and divided into positive group(n=23)and negative group(n=32)according to PNI or not.Histogram parameters of apparent diffusion coefficient(ADC),perfusion fraction(f),true diffusion coefficient(D)and pseudo diffusion coefficient(D*)were obtained based on MR IVIM images,including the mean,skewness,kurtosis,entropy,as well as the 25th,50th and 75th percentiles(25th,50th,75th).The above IVIM histogram parameters were compared between groups,and those with significant difference were included in binary stepwise logistic regression analysis to screen independent factors for predicting PNI status of rectal cancer.Then a combined parameter model was established.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of IVIM histogram parameters and combined parameter model for predicting PNI of rectal cancer.Results ADCmean,ADC25th,ADC50th,ADC75th,fmean,f25th,f50th,f75th and Dentropy in positive group were all lower than those in negative group(all P＜0.05).AUC of the above parameters for predicting PNI of rectal cancer was 0.693,0.665,0.701,0.675,0.831,0.847,0.835,0.722 and 0.785,respectively.The sensitivity,specificity and AUC of combined parameter model based on f25th and Dentropy for predicting PNI of rectal cancer was 95.65％,65.63％and 0.897,respectively.Conclusion IVIM histogram parameters could be used to predict PNI of rectal cancer effectively.