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]

Objectives:QT interval prolongation during treadmill test exercise is one of the clinical feature of patients with long QT syndrome(LQTS).This study aimed to explore the feasibility and efficacy of treadmill exercise testing as an auxiliary diagnosis tool for LQTS in clinical practice.Methods:We enrolled normal healthy individuals,common cardiovascular disease patients,and clinically diagnosed or suspected LQTS patients,who underwent treadmill exercise test from July 2023 to July 2024 at Fuwai Hospital.A special treadmill exercise testing procedure was designed to record the QT interval correction(QTc)intervals of the twelve lead electrocardiogram at 6 time points when performing the exercise tablet,including supine,sitting,standing,peak exercise,and recovery at 1-minute and 4-minute.The differences in QTc intervals among healthy group,cardiovascular diseases group,and suspected LQTS group were compared.Results:A total of 80 cases were consecutively enrolled,including 37 normal healthy controls,25 patients with common cardiovascular disease,and 18 patients with suspected LQTS.The QTc intervals at 6 points did not differ significantly between normal healthy controls and patients with cardiovascular disease,with QTc intervals less than 480 ms at all measurement.For patients with suspected LQTS,67.7%(12/18)of these patients presented a QTc interval≥480 ms at the 4-minute during recovery period.Among them,5 cases were confirmed to have pathogenic gene mutations of LQTS by genetic testing(including 1 case with a lying electrocardiogram QTc interval of 489 ms diagnosed with LQTS 1 type and a QTc interval of 636 ms during the 4-minute recovery period after exercise);5 clinically diagnosed patients(negative or undetectable in genetic testing)with a Schwartz score≥4,and the remaining 2 patients had a Schwartz score of 3.The remaining 5/18 patients,include 2 patients with clinical Schwartz scores≥4 and 3 patients with clinical suspicion(Schwartz scores 2-3)had a 4 min QTc interval of 445-480 ms during exercise recovery.Another patient with clinical suspicion(Schwartz score 3)had a 4 min QTc interval of<445 ms during exercise recovery and a negative genetic test at a later stage.Receiver operating characteristic curve analysis showed a sensitivity of 83.3%and specificity of 98.4%for QTc interval≥482 ms during the 4-minute recovery period of exercise as the LQTS diagnostic cutoff.Conclusions:This study results suggest that this special treadmill exercise testing protocol is effective in identifying LQTS and has strong feasibility and generalizability for clinical practice.

Objective To initially explore the possibility of applying the fluorescence micro-optical sectioning tomography(fMOST)high-resolution 3D reconstruction system to the morphological study of the intestinal nervous system and to preliminarily establish a method for studying the morphology of the intestinal nervous system using this system.Methods fMOST high-resolution 3D reconstruction system was used to study the intestinal nervous system of C57BL/6 mice in detail.Based on this method,a new morphological method of the visceral nervous system of small animal models was explored at the single-cell level.Results Compared with the large intestine,the small intestine lacked the typical myenteric plexus(Auerbach),deep mucosal plexus(Henley),and submucosal superficial plexus(Meissner).Conclusion The result of this paper provide a clearer and systematic display of the anatomical structure of the enteric nervous system in C57BL/6 mice,and further clarify the similarities and differences between the enteric nervous system of mice and human,and provide a theoretical basis for its rational application in the study of digestive system diseases.The morphological study of fMOST high-resolution 3D reconstruction system is not limited to the central nervous system,but can be extended to the morphological study of multiple visceral nervous systems.

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]

Objectives:QT interval prolongation during treadmill test exercise is one of the clinical feature of patients with long QT syndrome(LQTS).This study aimed to explore the feasibility and efficacy of treadmill exercise testing as an auxiliary diagnosis tool for LQTS in clinical practice.Methods:We enrolled normal healthy individuals,common cardiovascular disease patients,and clinically diagnosed or suspected LQTS patients,who underwent treadmill exercise test from July 2023 to July 2024 at Fuwai Hospital.A special treadmill exercise testing procedure was designed to record the QT interval correction(QTc)intervals of the twelve lead electrocardiogram at 6 time points when performing the exercise tablet,including supine,sitting,standing,peak exercise,and recovery at 1-minute and 4-minute.The differences in QTc intervals among healthy group,cardiovascular diseases group,and suspected LQTS group were compared.Results:A total of 80 cases were consecutively enrolled,including 37 normal healthy controls,25 patients with common cardiovascular disease,and 18 patients with suspected LQTS.The QTc intervals at 6 points did not differ significantly between normal healthy controls and patients with cardiovascular disease,with QTc intervals less than 480 ms at all measurement.For patients with suspected LQTS,67.7%(12/18)of these patients presented a QTc interval≥480 ms at the 4-minute during recovery period.Among them,5 cases were confirmed to have pathogenic gene mutations of LQTS by genetic testing(including 1 case with a lying electrocardiogram QTc interval of 489 ms diagnosed with LQTS 1 type and a QTc interval of 636 ms during the 4-minute recovery period after exercise);5 clinically diagnosed patients(negative or undetectable in genetic testing)with a Schwartz score≥4,and the remaining 2 patients had a Schwartz score of 3.The remaining 5/18 patients,include 2 patients with clinical Schwartz scores≥4 and 3 patients with clinical suspicion(Schwartz scores 2-3)had a 4 min QTc interval of 445-480 ms during exercise recovery.Another patient with clinical suspicion(Schwartz score 3)had a 4 min QTc interval of<445 ms during exercise recovery and a negative genetic test at a later stage.Receiver operating characteristic curve analysis showed a sensitivity of 83.3%and specificity of 98.4%for QTc interval≥482 ms during the 4-minute recovery period of exercise as the LQTS diagnostic cutoff.Conclusions:This study results suggest that this special treadmill exercise testing protocol is effective in identifying LQTS and has strong feasibility and generalizability for clinical practice.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

In this work,with tris(4-aminophenyl)amine(TAPA)and 1,3,5-tris(4-formylphenyl)benzene(TFPB)as monomers,an imine-type porous organic polymer,TAPA-TFPB,was synthesized using a simple method under the catalysis of acetic acid.The material TAPA-TFPB was used as solid-phase extraction adsorbent and combined with ultra-performance liquid chromatography/quadrupole time-of-flight-tandem mass spectrometry(UHPLC-QTOF-MS)to establish a detection method for four kinds of nitrofuran metabolites(NFMs)residues in meat samples.The parameters of the adsorbent dosage,the pH value and volume of sample,and the type and volume of washing and eluent solvents were optimized,respectively.Under the optimal extraction conditions,low detection limits(0.11-1.60 μg/kg)were achieved for four kinds of NFMs.At three different spiked levels,the intra-day and inter-day precisions(Relative standard deviations)were 2.8% -10.9% and 4.3% -16.2%,respectively,and the spiked recoveries were 72.0% -107.2%.The results showed that the method chould be used for efficient extraction and analysis of trace NFMs residues in meat samples,indicating that TAPA-TFPB was a kind of promising SPE adsorbent.

OBJECTIVE: To explore short-term clinical efficacy of minimally invasive diagnosis and treatment system for hallux valgus in guiding the treatment of hallux valgus. METHODS: From March 2021 to November 2023, 68 patients (136 feet) with hallux valgus were treated under guidance of minimally invasive diagnosis and treatment system, including 12 males and 56 females;aged from 25 to 68 years old with an average of (42.5±8.5) years old, the course of disease ranged from 3.2 to 15.6 years with an average of (10.3±2.6) years. The changes of hallux valgus angle (HVA) and intermetatarsal angle (IMA), visual analog scale (VAS) and American Orthopaedic Foot Ankle Society (AOFAS) forefoot score were recorded and compared before operation and 12 months after operation. RESULTS: Sixty-five patients (130 feet) were followed up for 12 to 15 months with an average of (13.8±0.5) months, 3 patients (6 feet) were not followed up as required. HVA and IMA improved from (35.5±3.5) ° and (12.5±2.0) ° before operation to (10.5±2.5) ° and (8.5±1.5) °12 months after operation, respectively, with statistically significant differences (P<0.05);VAS decreased from (5.5±1.2) before operation to (1.2±0.5) at 12 months after operation, and the difference was statistically significant (P<0.05);AOFAS forefoot score increased from (50.6±5.1) before operation to (93.8±5.6) at 12 months after operation, with a statistically significant difference (P<0.05). Among them, 102 feet were got excellent result, 24 feet good, and 4 feet fair. Two patients were developed calf intermuscular vein thrombosis, and were cured after 3 months of symptomatic treatment. CONCLUSION Under the guidance of minimally invasive diagnosis and treatment system for hallux valgus, the treatment of HV could obviously improve HVA and IMA, and significantly alleviate pain symptoms, and accelerate functional recovery.
Humans ; Hallux Valgus/diagnosis* ; Male ; Female ; Middle Aged ; Adult ; Aged ; Minimally Invasive Surgical Procedures/methods* ; Treatment Outcome

PURPOSE: To investigate the protective effect of sub-hypothermic mechanical perfusion combined with membrane lung oxygenation on ischemic hypoxic injury of yorkshire brain tissue caused by traumatic blood loss. METHODS: This article performed a random controlled trial. Brain tissue of 7 yorkshire was selected and divided into the sub-low temperature anterograde machine perfusion group (n = 4) and the blank control group (n = 3) using the random number table method. A yorkshire model of brain tissue injury induced by traumatic blood loss was established. Firstly, the perfusion temperature and blood oxygen saturation were monitored in real-time during the perfusion process. The number of red blood cells, hemoglobin content, NA⁺, K⁺, and Ca²⁺ ions concentrations and pH of the perfusate were detected. Following perfusion, we specifically examined the parietal lobe to assess its water content. The prefrontal cortex and hippocampus were then dissected for histological evaluation, allowing us to investigate potential regional differences in tissue injury. The blank control group was sampled directly before perfusion. All statistical analyses and graphs were performed using GraphPad Prism 8.0 Student t-test. All tests were two-sided, and p value of less than 0.05 was considered to indicate statistical significance. RESULTS: The contents of red blood cells and hemoglobin during perfusion were maintained at normal levels but more red blood cells were destroyed 3 h after the perfusion. The blood oxygen saturation of the perfusion group was maintained at 95% - 98%. NA⁺ and K⁺ concentrations were normal most of the time during perfusion but increased significantly at about 4 h. The Ca²⁺ concentration remained within the normal range at each period. Glucose levels were slightly higher than the baseline level. The pH of the perfusion solution was slightly lower at the beginning of perfusion, and then gradually increased to the normal level. The water content of brain tissue in the sub-low and docile perfusion group was 78.95% ± 0.39%, which was significantly higher than that in the control group (75.27% ± 0.55%, t = 10.49, p < 0.001), and the difference was statistically significant. Compared with the blank control group, the structure and morphology of pyramidal neurons in the prefrontal cortex and CA1 region of the hippocampal gyrus were similar, and their integrity was better. The structural integrity of granulosa neurons was destroyed and cell edema increased in the perfusion group compared with the blank control group. Immunofluorescence staining for glail fibrillary acidic protein and Iba1, markers of glial cells, revealed well-preserved cell structures in the perfusion group. While there were indications of abnormal cellular activity, the analysis showed no significant difference in axon thickness or integrity compared to the 1-h blank control group. CONCLUSIONS Mild hypothermic machine perfusion can improve ischemia and hypoxia injury of yorkshire brain tissue caused by traumatic blood loss and delay the necrosis and apoptosis of yorkshire brain tissue by continuous oxygen supply, maintaining ion homeostasis and reducing tissue metabolism level.
Animals ; Perfusion/methods* ; Disease Models, Animal ; Brain Injuries/etiology* ; Swine ; Male ; Hypothermia, Induced/methods*

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]