Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

PURPOSE: Although there are significant differences between China and the United States (US) in trauma medical services, there has been no direct comparative research on the epidemiological data of trauma centers between the 2 countries. This study aims to fill this research gap by directly comparing trauma centers in China and the US, providing valuable data and insights for the development of trauma centers in both countries, promoting academic exchange and cooperation internationally, and enhancing the level of global trauma medical care. METHODS: This is a multicenter retrospective descriptive study. Data were collected for trauma patients with an injury severity score ≥16 treated from September 2013 to September 2019 at 2 hospital trauma centers in these 2 countries. Detailed clinical data (including injury mechanism, age, injury site, injury severity score, pre-hospital transport time, whether blood transfusion was performed, whether resuscitative thoracotomy was conducted, hospital and intensive care unit stay duration, the number of organ donor patients, mortality rates, and costs) were meticulously compiled and retrospectively analyzed to identify differences between the 2 trauma centers. The comparison was conducted using SPSS 23 software. Continuous variables are reported as median (Q₁, Q₃), and Mann Whitney U test is used to compare the median of continuous variables. Use clinically relevant critical points to classify continuous variables, with categorical variables represented as n (%), and comparisons were made between the 2 groups using the χ² test or Fisher's exact test. Statistical significance was defined as a 2-sided p < 0.05. RESULTS: These results point to significant differences in trauma center capacity, pre-hospital transport times, treatment procedures, hospital stay duration, mortality rates, and costs between the 2 centers. The volume of patients in trauma centers is less in China (2465 vs. 5288). Pre-hospital transport time was notably longer in China (180 min vs. 14 min), and the rate of emergency blood transfusions was lower in China (18.4% vs. 50.6%), Emergency thoracotomy was not performed in China but was conducted in 9.8% of cases in the US. Hospitalization costs were significantly lower in China than in the US ($5847 vs. $75,671). CONCLUSION There are clear differences in trauma center capacity (number of patients treated), pre-hospital transport time, age distribution of injured patients, injury mechanisms, injury sites, whether emergency thoracotomy is performed, hospital costs, and length of stay between the 2 trauma centers in China and America. Understanding these differences can help us further recognize the characteristics of Eastern and Western trauma patients.
Humans ; China/epidemiology* ; Trauma Centers/statistics & numerical data* ; Retrospective Studies ; United States/epidemiology* ; Male ; Female ; Wounds and Injuries/therapy* ; Middle Aged ; Adult ; Injury Severity Score ; Length of Stay/statistics & numerical data* ; Treatment Outcome

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective To investigate the mechanism of Achyranthoside Ⅰ inhibits pyroptosis in chondrocytes through the nuclear factor-κB(NF-κB)/NOD receptor protein structure domain related proteins 3(NLRP3)/cystine containing aspartate specific proteins-1(caspase-1)signaling pathway.Methods Primary mouse chondrocytes were divided into blank group(phosphate buffered solution with the same volume),model group[10 ng·mL-1 interleukin-1β(IL-1 β)],control group(10 ng·mL-1 IL-1β+20 μmol·L-1 celecoxib)and experimental group(10 ng·mL-1 IL-1β+3 μg·mL-1 Achyranthoside Ⅰ).After 24 hours of intervention,the cell proliferation was measured by cell counting kit 8,the levels of superoxide dismutase(SOD),malondialdehyde(MDA),IL-1 and IL-6 were detected by enzyme-linked immunosorbent assay,the protein expression levels of NF-κB p65,NLRP3 and caspase-1 were detected by Western Blot.Results The apoptosis rates in experimental,control,model and blank groups were(13.34±0.61)％,(15.64±1.01)％,(21.81±1.10)％and 0;the SOD levels were(147.03±16.49),(130.09±7.33),(122.03±10.71)and(164.40±22.74)nU·mL-1;the MDA levels were(6.43±0.71),(7.63±1.01),(8.89±1.84)and(5.69±0.81)nmol·L-1;the IL-1 levels were(338.69±40.95),(361.78±32.15),(391.44±30.59)and(289.23±25.19)pg·mL-1;the IL-6 levels were(89.96±8.81),(101.10±11.59),(120.39±14.71)and(60.29±6.03)pg·mL-1;the relative expression levels of NF-κB p65 were 0.68±0.05,0.97±0.05,1.26±0.05 and 0.57±0.05;the relative expression levels of NLRP3 were 0.71±0.08,1.02±0.10,1.50±0.06 and 0.31±0.05;the relative expression levels of caspase-1 were 0.70±0.07,1.29±0.08,1.66±0.07 and 0.51±0.07,respectively.Compared with the model group,the differences of above indexes were statistically significant in the experimental group(all P＜0.05).Conclusion Achyranthoside Ⅰ can improve the oxidative stress status induced by IL-1 β in chondrocytes,reduce the expression of proteins related to the NF-κB signaling pathway,and thereby decrease the occurrence of caspase-1 dependent pyroptosis,providing a protective effect on chondrocytes.

Sacroiliac complex injuries are commonly seen in high-energy pelvic fractures. The injuries make a big difference in treatment patterns due to the diverse injury types, posing considerable challenges in formulating optimal treatment strategies, and hence are persistent clinical difficulties in orthopedic trauma. The clinical management of sacroiliac complex injuries presents several key challenges such as a non-negligible rate of missed diagnoses in associated vascular and visceral injuries, absence of standardized protocols for surgical approaches and reduction-fixation strategies across different injury patterns, and ongoing controversies regarding surgical indications and optimal timing for patients combined with concomitant lumbosacral plexus injuries. Currently, no systematic clinical guidelines are available for the diagnosis and treatment of sacroiliac complex injuries both domestically and internationally. To this end, the Pelvic and Acetabular Surgery Group, Orthopedic Branch, China International Exchange and Promotive Association for Medical and Health Care and Orthopedic Physician Branch, Chinese Medical Doctor Association organized a panel of domestic experts in the field to develop the Clinical guideline for the diagnosis and treatment of sacroiliac complex injuries ( version 2025), based on evidence-based medicine and adhering to the principles of scientific rigor, clinical applicability, and innovation. These guidelines provided 11 recommendations covering diagnosis, therapeutic principles and techniques, management protocols for lumbosacral plexus injuries, outcome evaluation, and postoperative rehabilitation pathways, etc., aiming to standardize the clinical management of sacroiliac complex injuries.