Objective To explore the brain damage of SD rats under different time points of hypobaric hypoxia exposure.Methods A rat high-altitube cerebral edema(HACE)model was constructed by simulating an altitude of 6 000 m in a hypobaric hypoxia animal experimental chamber.Thirty-six SD male rats were randomly divided into the control group and the hypobaric hypoxia exposure 3,7 and 14 d groups,with 9 rats in each group.Except for the control group,the rats in each group were continuously exposed to hypobaric hypoxia for 3,7,and 14 d.At the end of the modeling period,serum was collected by blood sampling via the abdominal aorta,and brain tissue samples were taken.The wet-to-dry ratio(W/D)of brain tissue was calculated,and the levels of relevant oxidative enzymes in serum and brain tissue were measured.The expression levels of hypoxia-inducible factor-1α(HIF-1α)and aquaporin 4(AQP4)mRNAs in brain tissue were detected by real-time fluorescence quantitative polymerase chain reaction.Results The W/D of brain tissues in the control group and the group exposed to hypobaric hypoxia for 3,7 and 14 d were 4.46±0.12,4.98±0.16,5.07±0.18 and 4.95±0.07;the superoxide dismutase contents were(111.86±2.45),(90.73±1.48),(79.64±2.56)and(55.33±1.45)U·g-1;the glutathione contents were(126.91±5.18),(125.26±1.53),(56.20±2.17)and(122.73±1.78)μg·mL-1;the malondialdehyde contents were(230.94±2.00),(362.65±3.28),(407.34±3.47)and(237.50±1.59)nmol·g-1;the relative expression levels of HIF-1 α mRNA were 1.00±0,2.99±0.49,4.72±0.49 and 1.91±0.28;the relative expression levels of AQP4 mRNA were 1.00±0,2.62±0.34,8.38±0.84 and 5.27±0.42,respectively.Statistically significant differences were found between the above indexes in the 3,7 and 14 d of hypobaric hypoxia exposure group compared with the control group(P＜0.05,P＜0.01).Conclusion Different time of hypobaric hypoxia exposure can up-regulate the expression of AQPs proteins in HACE rats and cause the disruption of the blood-brain barrier,and the HACE model constructed in the hypobaric hypoxia chamber with 6 000 m intervention for 7 d was more stable.

Objective To explore the brain damage of SD rats under different time points of hypobaric hypoxia exposure.Methods A rat high-altitube cerebral edema(HACE)model was constructed by simulating an altitude of 6 000 m in a hypobaric hypoxia animal experimental chamber.Thirty-six SD male rats were randomly divided into the control group and the hypobaric hypoxia exposure 3,7 and 14 d groups,with 9 rats in each group.Except for the control group,the rats in each group were continuously exposed to hypobaric hypoxia for 3,7,and 14 d.At the end of the modeling period,serum was collected by blood sampling via the abdominal aorta,and brain tissue samples were taken.The wet-to-dry ratio(W/D)of brain tissue was calculated,and the levels of relevant oxidative enzymes in serum and brain tissue were measured.The expression levels of hypoxia-inducible factor-1α(HIF-1α)and aquaporin 4(AQP4)mRNAs in brain tissue were detected by real-time fluorescence quantitative polymerase chain reaction.Results The W/D of brain tissues in the control group and the group exposed to hypobaric hypoxia for 3,7 and 14 d were 4.46±0.12,4.98±0.16,5.07±0.18 and 4.95±0.07;the superoxide dismutase contents were(111.86±2.45),(90.73±1.48),(79.64±2.56)and(55.33±1.45)U·g-1;the glutathione contents were(126.91±5.18),(125.26±1.53),(56.20±2.17)and(122.73±1.78)μg·mL-1;the malondialdehyde contents were(230.94±2.00),(362.65±3.28),(407.34±3.47)and(237.50±1.59)nmol·g-1;the relative expression levels of HIF-1 α mRNA were 1.00±0,2.99±0.49,4.72±0.49 and 1.91±0.28;the relative expression levels of AQP4 mRNA were 1.00±0,2.62±0.34,8.38±0.84 and 5.27±0.42,respectively.Statistically significant differences were found between the above indexes in the 3,7 and 14 d of hypobaric hypoxia exposure group compared with the control group(P＜0.05,P＜0.01).Conclusion Different time of hypobaric hypoxia exposure can up-regulate the expression of AQPs proteins in HACE rats and cause the disruption of the blood-brain barrier,and the HACE model constructed in the hypobaric hypoxia chamber with 6 000 m intervention for 7 d was more stable.

Objective:To compare the aortic remodeling of the Fabulous stent system and standard thoracic aortic endovascular repair (TEVAR) on distal aorta type B aortic dissection (TBAD). Methods:The prospective data collected between Dec 2017 and Oct 2019 from 134 patients with type B aortic dissection (TBAD) who underwent treatment with the "Fabulous" stent system, and retrospective data from 159 TBAD patients receiving standard TEVAR from corresponding multicenter. By using propensity score matching analysis, we compared the prognosis and aortic remodeling outcomes in patients undergoing Fabulous and standard TEVAR treatments during a 1-year postoperative follow-up.Results:In this study, 62 patients in Fabulous group and 62 patients in standard TEVAR were included.There were no significant statistical differences in baseline characteristics between the two groups. In terms of aortic remodeling in bare stent region, Fabulous group had better change trends of diameter of true lumen [10.6 (4.4, 14.5) mm vs. 4.7 (0.9, 10.7) mm, P=0.001] and false lumen [-24.2 (-30.5, -4.9) mm vs. 0.7 (-11.8, 2.3) mm, P<0.001] than those in the standard TEVAR group. The rate of complete false lumen thrombosis was also higher in the Fabulous group (62.9% vs. 37.1%, P=0.042). Conclusion:The Fabulous stent system, when compared to standard TEVAR surgery, demonstrates good aortic remodeling outcomes in the distal aorta.

Objective:To construct an explainable artificial intelligence(AI) model of risk characteristics of papillary thyroid carcinoma(PTC), and to explore its value of it combined with clinical features in predicting cervical lymph node metastasis(CLNM) in PTC patients.Methods:From January 2021 to September 2022, 422 patients(422 nodules) with pathologically confirmed PTC underwent thyroidectomy and neck lymph node dissection in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively collected, the patients were randomly divided into training set and test set according to the ratio of 7∶3. Ultrasonographic features highly correlated with PTC risk characteristics were extracted by traditional machine learning method, and an intelligent prediction model with optimal probability of risk characteristics was established. Then, a risk model for predicting CLNM of PTC patients was constructed in combination with clinical features. The diagnostic effectiveness of the model was evaluated by drawing a ROC curve and calculating the area under curve (AUC).Results:In the AI explaineable model of PTC risk characteristics in the test set, the intelligent diagnosis model of calcification based on logistic regression classification showed the highest diagnostic efficiency, with an AUC of 0.87 ( P<0.05). Compared with the probability model of risk characteristic of PTC alone, the comprehensive model combined with clinical characteristics showed higher diagnostic efficiency in predicting CLNM of PTC patients, with AUC of 0.97, diagnostic critical value of 0.15, corresponding accuracy, sensitivity and specificity of 92.65%, 92.76% and 92.54%, respectively (all P<0.05). Conclusions:The explaineble risk characteristics of PTC AI model combined with clinical features can effectively predict the cervical lymph node metastasis of PTC, and then provide effective information for clinical decision-making of PTC patients.

Objective:To explore the diagnostic value of different machine learning models and optimal machine learning model combined with clinical data for diagnosing Hashimoto′s thyroiditis (HT).Methods:The thyroid gland images of 643 patients with 643 thyroid nodules who underwent preoperative ultrasound examination and had pathological results in the Second Affiliated Hospital of Xi′an Jiaotong University from December 2018 to March 2024 were retrospectively collected, and the images were divided into training set and test set according to a ratio of 7 to 3. Twenty ultrasound imaging omics models were constructed using pairwise combination of 5 feature screening components and 4 classifiers. The area under the curve (AUC) of each model in the test set was compared. Meanwhile, 3 basic network models were respectively used to construct deep learning models for diagnosing HT, and the diagnostic efficacies of the deep learning models and the ultrasound imaging omics models for HT were compared. The model with the greatest efficacy was selected as the optimal machine learning model. Further, the optimal machine learning model was combined with clinical data to construct a combined model. The ROC curves were plotted to compare the diagnostic efficacy of the optimal machine learning model and the combined model for HT.Results:In the comparison of the efficacies of ultrasound imaging omics models and deep learning models in diagnosing HT, the efficacy of stable feature screening-logistic regression (LR) model was the greatest, and the accuracy, sensitivity and specificity of using the LR model in diagnosing HT in the test set were 78%, 75%, 74%, respectively, with an AUC of 0.82(95% CI=0.76-0.88). After combining the LR model with clinical data, the accuracy, sensitivity, and specificity of the combined model in the test set were 87%, 74%, and 95%, respectively, with an AUC of 0.91(95% CI=0.87-0.95), which was strongly consistent with pathology (Kappa value=0.708, P<0.001). Conclusions:The optimal machine learning model (LR model) constructed in this study demonstrates a strong ability to diagnose HT and can accurately detect patients with atypical ultrasound manifestations of HT. The combination with clinical data can improve its diagnostic efficacy with higher accuracy and specificity.

Objective:To explore the diagnostic value of different machine learning models and optimal machine learning model combined with clinical data for diagnosing Hashimoto′s thyroiditis (HT).Methods:The thyroid gland images of 643 patients with 643 thyroid nodules who underwent preoperative ultrasound examination and had pathological results in the Second Affiliated Hospital of Xi′an Jiaotong University from December 2018 to March 2024 were retrospectively collected, and the images were divided into training set and test set according to a ratio of 7 to 3. Twenty ultrasound imaging omics models were constructed using pairwise combination of 5 feature screening components and 4 classifiers. The area under the curve (AUC) of each model in the test set was compared. Meanwhile, 3 basic network models were respectively used to construct deep learning models for diagnosing HT, and the diagnostic efficacies of the deep learning models and the ultrasound imaging omics models for HT were compared. The model with the greatest efficacy was selected as the optimal machine learning model. Further, the optimal machine learning model was combined with clinical data to construct a combined model. The ROC curves were plotted to compare the diagnostic efficacy of the optimal machine learning model and the combined model for HT.Results:In the comparison of the efficacies of ultrasound imaging omics models and deep learning models in diagnosing HT, the efficacy of stable feature screening-logistic regression (LR) model was the greatest, and the accuracy, sensitivity and specificity of using the LR model in diagnosing HT in the test set were 78%, 75%, 74%, respectively, with an AUC of 0.82(95% CI=0.76-0.88). After combining the LR model with clinical data, the accuracy, sensitivity, and specificity of the combined model in the test set were 87%, 74%, and 95%, respectively, with an AUC of 0.91(95% CI=0.87-0.95), which was strongly consistent with pathology (Kappa value=0.708, P<0.001). Conclusions:The optimal machine learning model (LR model) constructed in this study demonstrates a strong ability to diagnose HT and can accurately detect patients with atypical ultrasound manifestations of HT. The combination with clinical data can improve its diagnostic efficacy with higher accuracy and specificity.

A patient with SARS-CoV-2 infection was adimitted to Shanghai Shibei hospital of Jing'an District in early 2023. According to the patient's complaits， clinical manifestations， physical symptoms， laboratory examination， radiological image results， plus lumbar puncture， the patient was diagnosed with novel coronavirus encephalitis. The patient was discharged from the hospital after a combined treatment of Chinese and western medicine.

Objective:To explore the risk factors and outcomes of central nervous system(CNS)complications associated with hematopoietic stem cell transplantation(HSCT).Methods:A total of 550 recipient after HSCT in the department of hematology of Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 1 2019 to August 31 2021were enrolled.According to the occurrence of CNS complications, they were divided into the CNS group(24 cases)and the non CNS group(526 cases). The clinical information and prognosis were compared.We further analyzed the risk factors associated with CNS complications, and conducted multivariate logistic regression on statistically significant indicators.Cox regression analysis is conducted on prognostic factors such as age, gender and risk degree.Results:A total of 550 recipients were enrolled, of which 330 underwent allo-HSCT, and others received auto-HSCT.A total of 24 cases (4.36%)had CNS complications, of which 4 cases had 2 types of CNS complications.The type of CNS complications included intracranial infection(8 cases, 28.57%), transplantation-associated thrombotic microangiopathy(TA-TMA)(6 cases, 21.43%), central tumor invasion(4 cases, 14.29%), intracranial hemorrhage(4 cases, 14.29%), leucodystrophy(2 cases, 7.14%)and unexplained encephalopathy(4 cases, 14.29%). Logistic regression analysis of risk factors related to CNS complications showed that, Platelet implantation time( β=0.084, OR=1.088, P=0.048), CMV infection( β=1.295, OR=3.65, P=0.008)is positively correlated with the occurrence of CNS complications in HSCT recipients but age( β=-0.052, OR=0.949, P=0.004)is negatively correlated with it.Nine of the 24 cases(37.50%)who experienced CNS complications died, including 3 cases of intracranial infection, 3 cases of cerebral hemorrhage, 2 cases of TMA, and 1 case of unexplained encephalopathy.Platelet implantation time is an independent risk factor for poor prognosis of CNS complications in HSCT recipients. Conclusions:Our results indicated that, age, CMV infection and platelet implantation time were associated with the occurrence of CNS complications after HSCT.Platelet implantation time is an independent risk factor for poor prognosis of CNS complications in HSCT recipients.

OBJECTIVE To study the metabolites derived from the ethanol extract from the leaves of Dimocarpus longan preliminarily in rats in vivo ，and to provide reference for elucidating the possible metabolic mechanism of the leaves of D. longan in lowering blood glucose . METHODS Ultra high performance liquid chromatography quadrupole time -of-flight mass spectrometry （UPLC-Q-TOF-MS/MS） was adopted by taking ethanol extract of D. longan leaves，the feces and urine of rats at 0-72 h and 0-48 h after intragastric administration of 33.8 g/kg ethanol extract of D. longan leaves（by extract ），the feces and urine of rats at the corresponding time after intragastric administration of normal saline （blank control ） as samples . The accurate relative molecular weight ，formula and fragment information of the compounds were collected ， and the compounds were speculated and i dentified by matching with the database and spectrum library of the instrument ，and comparing with the reference substance and relevant literature . RESULTS A total of eight compounds were identified in urine and feces of rats ，including 2 prototype components and 6 metabolites. Three compounds （including two prototype components as quercetin ，luteolin and one metabolite as luteolin or kaempferol） in feces of rats were identified ；five compounds （all metabolites ） in urine of rats were identified ，involving metabolites of quercetin ，luteolin or kaempferol . Metabolites mainly included the products of methylation ，glucuronidation and oxidation. CONCLUSIONS After intragastric administration ，the ethanol extract from the leaves of D. longan is mainly metabolized in rats through methylation ，glucuronidation and other pathways . The identified compounds are mostly metabolites of quercetin and luteolin .

OBJECTI VE To optimize the extraction technology of the leaves of Dimocarpus longan according to flavonoids and phenolic acids. METHODS The contents of gallic acid ，protocatechuic acid ，ethyl gallate ，quercetin，luteolin and kaempferol in the leaves of D. longan were determined by HPLC. Based on single factor test ，with the ethanol volume fraction ，solid-liquid ratio and extraction time as factors ，using comprehensive scores of the contents of above six components as indexes ，the extraction technology of the leaves of D. longan was optimized by Box-Behnken response surface methodology. RESULTS The optimal extraction technology included ethanol volume fraction of 100％，solid-liquid ratio of l ∶ 7（g/mL），extraction time of 90 min， extraction temperature of 80 ℃. After 3 times of validation tests ，the average comprehensive score was 97.54（RSD＝0.33％，n＝ 3），relative error of which with predicted score （99.05）was 1.55％. CONCLUSIONS Box-Behnken response surface methodology combined with multi-index comprehensive score can be used for the extraction technology of the leaves of D. longan ，and the optimized extraction technology is stable and feasible.