Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Teicoplanin,currently one of the preferred drugs for treating infections caused by multidrug-resistant Staphylococcus aureus and Enterococcus in clinical practice,has a high plasma protein binding rate.Most of the drug binds to protein after entering the body,only a portion of the drug exists in a free form.Since the free drug can pass through biological membranes and enter the target tissue to exert its therapeutic effect,monitoring the free teicoplanin blood concentration is clinically important and plays a role in guiding individualized drug therapy.This article summarizes and compares the current methods for determining the concentration of free teicoplanin in the blood,and analyzes their advantages and disadvantages.Based on this,it analyzes the possible factors affecting the concentration of free teicoplanin in the blood,providing a reference for the development of new methods for detecting free teicoplanin concentration and providing more accurate individualized medication plans based on free concentration.

Objective:To access the relation of aortic arch calcification (AoAC) volume based on parathyroid SPECT/CT imaging with hungry bone syndrome (HBS) in patients with renal secondary hyperparathyroidism (SHPT) after parathyroidectomy (PTX).Methods:From June 2015 to May 2024, the imaging and clinical data of 89 renal SHPT patients (52 males, 37 females, age: (51.5±10.3) years) who underwent parathyroid 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT/CT in Affiliated Jiangyin Hospital of Nantong University were retrospectively analyzed. The AoAC volume was measured by Volume software of SPECT/CT system. The patients were divided into HBS group and non-HBS group, and the independent-sample t test, Mann-Whitney U test were used to compare differences of various indicators between the two groups. Logistic regression was used to analyze the influencing factors of HBS. Results:All 89 patients underwent PTX successfully, 50 patients (56.2%) were with HBS and 39 patients (43.8%) were not. The differences of age ((47.9±9.9) vs (56.1±9.0) years, t=-3.98, P<0.001), AoAC volume (0.36(0.02, 1.30) vs 1.57(0.37, 3.77)cm 3, Z=-3.17, P=0.002), preoperative alkaline phosphatase (ALP) (345.75(218.50, 632.50) vs (203.13±114.57)U/L, Z=-4.69, P<0.001), preoperative parathyroid hormone (147.85(109.83, 227.40) vs (135.58±51.14)pmol/L, Z=-2.34, P=0.019) and preoperative serum corrected calcium ((2.38±0.21) vs 2.54(2.39, 2.62)mmol/L, Z=-3.09, P=0.002) between HBS group and non-HBS group were significant. Logistic regression analysis showed that the AoAC volume ((odds ratio, OR)=0.628, 95% CI: 0.427-0.924, P=0.018), preoperative ALP ( OR=1.007, 95% CI: 1.002-1.012, P=0.010) and preoperative serum corrected calcium ( OR=0.041, 95% CI: 0.003-0.545, P=0.016) were independent risk factors for HBS. Conclusion:The AoAC volume measured by preoperative SPECT/CT can be used for an effective criteria of evaluating HBS in patients with renal SHPT after PTX, and AoAC volume may be one of independent risk factors for HBS.

Objective To evaluate the value of radiomics models and comprehensive models based on multiparametric magnetic resonance imaging(mpMRI)in predicting the expression of human epidermal growth factor receptor 2(HER2)in bladder cancer(BCa).Methods A total of 76 pathologically confirmed BCa patients undergoing pelvic mpMRI during Jan.2022 and Nov.2024 at the Affiliated Zhongda Hospital of Southeast University were retrospectively included.After the volume of interest(VOI)was sketched,7 modal features were obtained,including T2WI,DWI,DCE,T2WI+DWI,T2WI+DCE,DWI+DCE,and T2WI+DWI+DCE,which were analyzed with logistic regression to obtain the predictive values.After that,the best sequences were screened with receiver operating characteristic(ROC)curves,and then combined with support vector machine,logistic regression,K-nearest neighbor,plain Bayes and adaptive enhancement,to construct the radiomics prediction models.Logistic regression analysis was used to screen the predictors of high HER 2 expression and to construct a comprehensive prediction model and a nomogram.Finally,decision curve analysis(DCA)was used to quantify the clinical benefits.Results Among the radiomics models based on the T2WI+DWI+DCE sequence,the AdaBoost model demonstrated the best predictive performance,with area under the ROC curve(AUC)being 0.863(95％CI:0.807-0.920)in the training set and 0.716(95％CI:0.601-0.830)in the validation set.Based on radiomics features and clinical imaging characteristics,logistic regression analysis identified tumor pedicle and risk group as the predictors of high HER2 expression.The comprehensive prediction model based on the two factors achieved the AUC of 0.869(95％CI:0.772-0.965)in the training set and 0.875(95％CI:0.712-0.986)in the validation set.Conclusion The radiomics model based on the T2WI+DWI+DCE sequence has high accuracy in predicting HER2 expression,outperforming single-sequence models.The nomogram based on the comprehensive prediction model has high clinical decision-making efficacy and is useful for non-invasive identification of HER2 expression.

Teicoplanin,currently one of the preferred drugs for treating infections caused by multidrug-resistant Staphylococcus aureus and Enterococcus in clinical practice,has a high plasma protein binding rate.Most of the drug binds to protein after entering the body,only a portion of the drug exists in a free form.Since the free drug can pass through biological membranes and enter the target tissue to exert its therapeutic effect,monitoring the free teicoplanin blood concentration is clinically important and plays a role in guiding individualized drug therapy.This article summarizes and compares the current methods for determining the concentration of free teicoplanin in the blood,and analyzes their advantages and disadvantages.Based on this,it analyzes the possible factors affecting the concentration of free teicoplanin in the blood,providing a reference for the development of new methods for detecting free teicoplanin concentration and providing more accurate individualized medication plans based on free concentration.

Objective:To evaluate the diagnostic performance of the Vesical Imaging-Reporting and Data System（VI-RADS）based on multiparametric magnetic resonance imaging（mp-MRI）for muscle-invasive bladder cancer（MIBC）.Methods:A systematic search was conducted in PubMed，Web of Science，and Embase databases for studies published between September 2018 and December 2023 that investigated the use of VI-RADS for diagnosing MIBC. Inclusion criteria were studies utilizing mp-MRI-based VI-RADS scoring to determine MIBC. Exclusion criteria were studies with fewer than 10 patients，overlapping study populations，or those failing to assess the diagnostic performance of VI-RADS for MIBC. After quality assessment，RevMan 5.4 and Stata 15.1 were used to calculate pooled sensitivity and specificity，generate forest plots and summary receiver operating characteristic（SROC）curves，and determine the area under the curve（AUC）. Publication bias was assessed using Deeks funnel plot. Heterogeneity was evaluated using the I2 statistic，with meta-regression and subgroup analyses to explore its sources. Results:Twenty-nine studies involving 3 577 patients were included. At a VI-RADS cutoff of 3，the pooled sensitivity and specificity for MIBC diagnosis were 93%（95%CI 0.90-0.95）and 82%（95%CI 0.76-0.88），respectively. At a cutoff of 4，these values were 83%（95%CI 0.78-0.87）and 93%（95%CI 0.90-0.95）. The hierarchical SROC（HSROC）AUCs were 0.95 and 0.94 for cutoffs of 3 and 4，respectively. Subgroup and meta-regression analyses revealed that at a cutoff of 3，patient sample size，study design，MRI field strength，number of radiologists，surgical approach，and DWI/DCE imaging planes contributed to sensitivity heterogeneity（ P < 0.05）. All factors except study design and DWI plane were sources of specificity heterogeneity（ P < 0.05）. At a cutoff of 4，all factors significantly influenced heterogeneity in both sensitivity and specificity（ P < 0.05）. Meta-regression confirmed that both cutoffs（3 and 4）were significant sources of heterogeneity（ P < 0.05）. Conclusions:VI-RADS demonstrates excellent diagnostic performance for MIBC at both cutoffs（3 and 4），with VI-RADS ≥ 3 showing superior sensitivity and VI-RADS ≥ 4 offering higher specificity. The cutoff of 3 provides better overall diagnostic efficacy.

Long-term spaceflight exposes astronauts to multiple extreme environmental factors, such as cosmic radiation, microgravity, social isolation, and circadian rhythm disruption, that markedly increase the risk of depressive symptoms, posing a direct threat to mental health and mission safety. However, the underlying biological mechanisms remain complex and incompletely understood. The potential mechanisms of spaceflight-induced depressive symptoms involve multiple domains, including alterations in brain structure and function, dysregulation of neurotransmitters and neurotrophic factors, oxidative stress, neuroinflammation, neuroendocrine system imbalance, and gut microbiota disturbances. Collectively, these changes may constitute the biological foundation of depressive in astronauts during spaceflight. Space-related stressors may increase the risk of depressive symptoms through several pathways: impairing hippocampal neuroplasticity, suppressing dopaminergic and serotonergic system function, reducing neurotrophic factor expression, triggering oxidative stress and inflammatory responses, activating the hypothalamic-pituitary-adrenal axis, and disrupting gut microbiota homeostasis. Future research should integrate advanced technologies such as brain-computer interfaces to develop individualized monitoring and intervention strategies, enabling real-time detection and effective prevention of depressive symptoms to safeguard astronauts' psychological well-being and mission safety.
Space Flight ; Humans ; Astronauts/psychology* ; Depression/physiopathology* ; Gastrointestinal Microbiome ; Weightlessness/adverse effects* ; Oxidative Stress ; Brain/physiopathology* ; Hypothalamo-Hypophyseal System ; Neuronal Plasticity ; Pituitary-Adrenal System

During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
Brain-Computer Interfaces ; Humans ; Space Flight ; Astronauts/psychology* ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems

Objective To construct risk models for predicting the occurrence of high altitude de-acclimatization syndrome(HADAS)in the population returning from the plateau to the plain based on different machine learning algorithms and validate the predicting efficiency of these models.Methods Field or online surveys were conducted on the individuals who had ended their high-altitude living and returned to the plain areas from November 2020 to February 2024.Basic information,chronic mountain sickness(CMS),HADAS symptoms and other data were collected.With the inclusion and exclusion criteria,totally 1 095 individuals were subjected and assigned into the modeling group.Positive events were defined as HADAS score>5.Then the modelling group was divided into a training set(n=766)and an internal test set(n=329)in a 7∶3 ratio.Least absolute shrinkage and selection operator(LASSO)regression was used to select independent variables.Risk prediction models for high-altitude adaptation symptoms were built based on 8 machine learning methods,including multiple factor logistic regression(LR),decision tree(DT),random forest(RF),eXtreme gradient boosting(XGB),support vector machine(SVM),K-nearest neighbor(KNN),light gradient boosting(LGB)and na?ve bayes(NB).The models were compared and evaluated using receiver operating characteristic(ROC)curves,calibration curves and confusion matrices in the internal test set.The final model was presented using a nomogram or Shapley additive explanations(SHAP)algorithm.In August 2024,another 132 individuals who returned to the plains and met the same criteria were recruited and served as the external validation group.Results There were 549 individuals(50.14%)out of the 1 095 subjects having HADAS symptoms.LASSO regression identified CMS score,age and duration of high-altitude residence as significant predictors.Among the 8 machine learning algorithms,the LR model was identified as the best,with an area under the curve(AUC)value of 0.819(95%CI:0.789～0.850)and 0.841(95%CI:0.799～0.884),and an F1 score of 0.801 in the internal test set,respectively,and the AUC value and F1 score of the LR model were the largest among the 8 models in the internal test set.Spiegelhalter Z test of the calibration curve of the LR model indicated that its P=0.703 in the training set while P=0.281 in the internal test set.The AUC value of the LR model was 0.867(95%CI:0.765～0.969)in the external validation set.Conclusion The LR model constructed based on indicators including CMS score,age and duration of high-altitude residence has a good overall performance in the internal test set,and good discriminating effect in the external validation set.The constructed nomogram is convenient for application.

Objective To explore the prevention effectiveness of a mode,conducting targeted corrective training based on motor dysfunction detected by military joint function screening,on military training injuries in new recruits during recruit basic training in order to cope with the high incidence of military training injuries among them.Method A military personnel joint function screening was conducted on the new recruits in a training base of Joint Logistics Support Force.Based on the results of screening,the new recruits with a single action score of 1 and a total score of<10 were subjected and served as corrective training participants.Through cluster sampling,the new recruits were randomly divided into an experimental group(n=223)and a control group(n=223).The control group were trained according to the regular training plan,while the experimental group completed their designated training tasks and a 2-month corrective training for motor dysfunction at the same time.The entire corrective training process was carried out by our key personnel who had received relevant training.Medical records of the medical security department of the experimental unit were collected and evaluated for the injury situation.The score of joint function screening was analyzed using independent sample t test.The incidence of training injuries was analyzed using Chi-square test or Fisher's exact test.Result After corrective training,the score of joint function was higher in the experimental group than the score before training(15.12±2.13 vs 10.58±2.83),and the score was also higher than that of the control group(15.12±2.13 vs 14.19±1.97,P<0.05).During the recruit basic training period,the incidence of training injuries was notably lower in the experimental group than the control group(12.5％vs 34.5％,Chi-square=5.469,P=0.001).Only for those who were injured during the training,11 people(39.2％)in the experimental group scored less than 10,which was obviously lower than the 49 people(63.6％)in the control group(Chi-square=4.972,P=0.026).Conclusion Our mode,corrective training based on the results of military personnel joint function screening,can effectively reduce the incidence of military training injuries in new recruits during recruit basic training,and exerts a good preventive effect against military training injuries.