Objective In response to the practical issues of pilot ground simulation ejection training relying on subjective evaluation,which requires high professionalism and lacks objectivity and systematicity,an objective evaluation system and method for ejection training based on pressure detection has been designed.Methods According to the rules of ejection training about posture,maneuver and time,and utilizing technologies such as pressure detection,infrared detection,and time-triggered detection,this paper designed separate modules for ejection posture detection,ejection timing detection,and comprehensive analysis and evaluation.These modules are closely integrated with the ejection training process,achieving objective evaluation of the ejection training.Results This system and method can provide a scientific and real-time objective evaluation of the posture,movements,and time in ejection training without affecting the normal organization and process.Conclusion The objective evaluation system can provide a new approach that is highly objective and easy to operate for comprehensively evaluating and enhancing the effectiveness of ejection training,as well as for scientifically conducting ejection training and assessment.

OBJECTIVE: To explore the genetic basis of a patient suspected for Loeys-Dietz syndrome (LDS). METHODS: A adult male patient with aneurysmal dilation of the aortic root identified during the treatment for chronic myeloid leukemia at Anzhen Hospital of Capital Medical University in 2021 was selected as the study subject. Clinical data of the patient were retrospectively collected. Peripheral blood samples were collected from the patient and his family members and subjected to whole-exome sequencing (WES). Candidate variant was verified by bioinformatic analysis, with a focus on the genes associated with hereditary aortic aneurysms. Candidate variant was validated by Sanger sequencing. The online SpliceAI software was used for the prediction of protein function. The results, combined with information from public databases, were used to classify the pathogenicity of the candidate variant according to the guidelines from the American College of Medical Genetics and Genomics (ACMG). This study was approved by the Ethics Committee of Beijing Anzhen Hospital (Ethics No. 2023163X). RESULTS: Imaging analysis revealed that the patient had aneurysmal dilation of the aortic root. Based on his clinical features and past history, a provisional diagnosis of LDS was established. WES revealed that the patient had harbored a heterozygous splice site variant c.206+2T>G in the SMAD3 gene (NM_005902). The variant was not reported in public databases and was predicted to be pathogenic by SpliceAI. Sanger sequencing showed that the variant was also present in the proband's mother, sister, nephew, and daughter, but not in his father. Based on the guidelines from the ACMG, the variant was classified as likely pathogenic (PVS1+PM2_Supporting). CONCLUSION The heterozygous splice site variant c.206+2T>G of the SMAD3 gene probably underlay the disease in this patient. Above discovery has enriched the mutational spectrum of LDS, which may facilitate delineation of the genotype-phenotype correlation and provide a basis for further risk stratification and personalized treatment of LDS.
Adult ; Humans ; Male ; Exome Sequencing ; Loeys-Dietz Syndrome/genetics* ; Mutation ; Pedigree ; Smad3 Protein/genetics*

Objective:To investigate the local hemodynamic spatial distribution of internal carotid artery stenosis and its correlation with plaque characteristics using high-resolution vessel wall imaging (HR-VWI) combined with computational fluid dynamics.Methods:This was a cross-sectional study. A retrospective analysis was conducted on the clinical and imaging data of 70 patients with moderate to severe stenosis at the initiation of the internal carotid artery in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and Tianjin First Central Hospital from March 2018 to June 2020. All patients underwent HR-VWI and CT angiography examinations. The parameters related to plaque characteristics, such as plaque length, maximum wall thickness, plaque volume, wall volume percentage and intraplaque hemorrhage (IPH) were measured and evaluated on HR-VWI images. CT angiography images were used to construct a local hemodynamic vascular model to measure various wall shear stress (WSS) derived parameters, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and transverse wall shear stress (transWSS), at the narrowest, proximal, and distal parts of the lesion. The Friedman test was used to analyze the difference of hemodynamic parameters in different parts of the lesion. Spearman correlation analysis was performed to assess the correlation between plaque burden and local hemodynamic parameters. Univariate and multivariate logistic regression analyses were used to explore the independent risk factors for predicting IPH.Results:Among the 70 patients, 25 patients with IPH and 45 patients without IPH. The overall differences in TAWSS, OSI, RRT and transWSS at the narrowest, proximal, and distal parts of the lesion in 70 patients were statistically significant ( P<0.05). The TAWSS and transWSS at the narrowest parts were significantly higher than those at the proximal and distal parts ( P<0.05). The OSI at the distal part was significantly higher than that at the narrowest and the proximal parts ( P<0.05). The RRT at the proximal part was significantly lower than that at the narrowest and the distal parts ( P<0.05). Spearman correlation analysis showed RRT at the distal part was correlated with plaque volume ( r s=0.249, P=0.044) and wall volume percentage ( r s=0.286, P=0.016), respectively. In a multivariate logistic regression showed plaque length ( OR=1.315, 95% CI 1.073-1.612, P=0.008) and TAWSS at the narrowest part ( OR=1.631, 95% CI 1.308-1.854, P=0.008) were independent risk factors for predicting IPH. Conclusions:The spatial distribution of local hemodynamics of moderate to severe stenosis at the initiation of the internal carotid artery is different, and the WSS parameters in different parts of the lesion have different effects on plaque volume, wall volume percentage and IPH.

Objective To enhance the efficiency and accuracy of post-marketing safety monitoring and evaluation of drugs in China by studying large language models-based patient medical record pharmacovigilance profiling techniques,providing scientific methods and technical support to ensure the safe use of drugs for patients.Methods This study constructs a pharmacovigilance profile that includes individual patient differences,medication details,and adverse reaction manifestations.It enhances a large language model with a knowledge graph in the field of pharmacovigilance and designs targeted prompts to guide the model to output pharmacovigilance profiles.Results Large language models demonstrate significant advantages in active monitoring,effectively processing and analyzing medical text data,and improving the monitoring and prediction capabilities of drug adverse reactions.Through the design of prompts,the model can more accurately depict patient pharmacovigilance profiles,providing decision support for medical professionals.Conclusions The study of large language model-based patient medical record pharmacovigilance profiling technology provides scientific evidence and technical support for the early detection and prevention of drug adverse reactions,helping to reduce medical costs,improve medical outcome prognoses,and opens new paths to ensure patient drug safety.

Haloacetic acids(HAAs),as a class of disinfection byproducts in drinking water,pose potential threats to human health,so the rapid,accurate and simultaneous detection of HAAs is of great significance for ensuring drinking water safety.Aiming at the challenges in HAAs detection and risk analysis,a novel method for synchronous rapid detection of seven kinds of HAAs in drinking water based on in situ derivatization technology and headspace gas chromatography was developed in this study.Through single-factor optimization experiments,the optimal reaction parameters for in situ derivatization were determined,including the type and dosage of salting-out agent,the acidity of reaction system,the amount of phase transfer catalyst,the dosage of derivatization agent,and the extraction solvent volume.Methodologic validation showed that the seven kinds of HAAs exhibited excellent linear relationships within their respective detection concentration ranges(R2>0.998).The method detection limits(MDLs)ranged from 0.04 to 0.33 μg/L,and the limits of quantification(LOQs)were between 0.14 and 1.34 μg/L.For real water samples,the average spiked recoveries of the seven HAAs ranged from 90.9%to 107.7%,with relative standard deviation(RSDs)between 1.55%and 6.49%,and the HAAs contents in all tested samples were below the limits specified in the Standards for Drinking Water Quality(GB 5749-2022)of China.This method was featured with simple operation,fast analysis speed,high sensitivity,and good accuracy,providing an efficient and reliable technical support for routine monitoring of HAAs contaminants in drinking water and showing promising application value for widespread promotion.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

Objective:To explore the genetic basis of a patient suspected for Loeys-Dietz syndrome (LDS).Methods:An adult male patient with aneurysmal dilation of the aortic root identified during the treatment for chronic myeloid leukemia at Anzhen Hospital of Capital Medical University in 2021 was enrolled as the study subject. Clinical data of the patient were retrospectively collected. Peripheral blood samples were collected from the patient and his family members and subjected to whole-exome sequencing (WES). Candidate variant was verified by bioinformatic analysis, with a focus on the genes associated with hereditary aortic aneurysms. Candidate variant was validated by Sanger sequencing. The online SpliceAI software was used for the prediction of protein function. The results, combined with information from public databases, were used to classify the pathogenicity of the candidate variant according to the guidelines from the American College of Medical Genetics and Genomics (ACMG). This study was approved by the Ethics Committee of Beijing Anzhen Hospital (Ethics No. 2023163X).Results:Imaging analysis revealed that the patient had aneurysmal dilation of the aortic root. Based on his clinical features and past history, a provisional diagnosis of LDS was established. WES revealed that the patient had harbored a heterozygous splice site variant c. 206+ 2T>G in the SMAD3 gene (NM_005902). The variant was not reported in public databases and was predicted to be pathogenic by SpliceAI. Sanger sequencing showed that the variant was also present in the patients mother, sister, nephew, and daughter, but not in his father. Based on the guidelines from the ACMG, the variant was classified as likely pathogenic (PVS1+ PM2_Supporting). Conclusion:The heterozygous splice site variant c. 206+ 2T>G of the SMAD3 gene probably underlay the disease of this patient. The discovery has enriched the mutational spectrum of LDS, which may facilitate delineation of the genotype-phenotype correlation and provide a basis for further risk stratification and personalized treatment of LDS.

Objective:To investigate the clinical characteristics and risk factors of airway mucus plugging in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).Methods:This was a retrospective cross-sectional study. A total of 322 hospitalized AECOPD patients admitted to the First Affiliated Hospital of Anhui Medical University from February 2023 to February 2025 were enrolled. Based on chest high-resolution computed tomography (HRCT) findings of airway mucus plugging, patients were classified into mucus plugging and non-mucus plugging groups. General and clinical data were collected, including age, sex, disease duration, smoking and alcohol history, comorbidities, number of acute exacerbations in the past year, routine blood tests, biochemical indices, pulmonary function, and pathogen detection. The incidence of airway mucus plugging in AECOPD patients was calculated, and differences in baseline characteristics, laboratory parameters, and pulmonary function between the two groups were compared. Logistic regression was used to identify independent risk factors for mucus plugging, and receiver operating characteristic (ROC) curves were plotted to evaluate the predictive value of relevant indicators.Results:Of the 322 enrolled patients, 87(27.02%) were found to have airway mucus plugging. Univariate analysis revealed statistically significant differences between the mucus plug group and the non-plug group in the following parameters (all P<0.05): body mass index (BMI), disease duration, smoking status, Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, modified British Medical Research Council (mMRC) dyspnea scale, COPD Assessment Test (CAT) score, frequency of acute exacerbations, neutrophil percentage, absolute lymphocyte count, lymphocyte percentage, albumin, C-reactive protein (CRP), activated partial thromboplastin time, fibrinogen, fibrin(ogen) degradation products, D-dimer, Aspergillus infection rate, percentage of forced expiratory volume in 1 second to predicted value (FEV 1%pred), ratio of FEV 1 to forced vital capacity (FEV 1/FVC), and percentage of maximal mid-expiratory flow to predicted value (MMEF 75/25%pred). Multivariate logistic regression analysis identified the following as independent risk factors for airway mucus plugs (all P<0.05): elevated CRP ( OR=1.022, 95% CI: 1.013-1.036), decreased albumin ( OR=0.891, 95% CI: 0.825-0.959), Aspergillus infection ( OR=1.774, 95% CI: 1.366-2.317), and reduced MMEF 75/25%pred value ( OR=0.978, 95% CI: 0.964-0.990). ROC curve analysis showed that the combined predictive model incorporating CRP, albumin, Aspergillus infection, and MMEF 75/25%pred had an area under the ROC curve (AUC) of 0.776(95% CI: 0.714-0.838), which was superior to each individual indicator alone, with AUCs of 0.721 for CRP, 0.687 for albumin, 0.579 for Aspergillus infection, and 0.631 for MMEF 75/25%pred. Conclusions:AECOPD patients with airway mucus plugging exhibit higher inflammatory markers, poorer nutritional status, a higher likelihood of Aspergillus infection, worse pulmonary function, and poorer prognosis. Aspergillus infection, elevated CRP, decreased albumin, and reduced MMEF 75/25%pred are independent risk factors for mucus plugs in AECOPD.

Objective:To examine the clinical features and genetic profiles of patients with thyroid peroxidase(TPO) gene mutations and provide diagnostic guidance for clinicians.Methods:A retrospective review of four patients with TPO mutations treated at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, from January 2014 to December 2023. Data on demographics, clinical manifestation, genotypes, treatment, and prognosis of these patients were analyzed.Results:Two males and two females, aged 13 to 27 years at diagnosis, presented with goiter as the initial symptom, with three cases menifesting during puberty. Laboratory findings showed mildly elevated or upper-limit-normal serum thyroid-stimulating hormone(TSH) levels, significantly increased free triiodothyronine/free thyroxine(FT 3/FT 4) ratios, and elevated thyroglobulin(TG) levels. Genetic testing revealed compound heterozygous pathogenic or likely pathogenic TPO mutations. Despite regular levothyroxine(L-T 4) therapy, goiter persisted. Three patients required thyroidectomy due to cosmetic or compressive symptoms, with pathology showing follicular hyperplasia. Conclusion:TPO mutations are characterized by adolescent-onset goiter, elevated FT 3/FT 4 ratios, and normal to high TSH. Genetic testing confirms the diagnosis. L-T 4 offers limited improvement, and surgery is often needed.