Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

Objective:To prepare the chitin/hyaluronic acid/collagen hydrogel loaded with mouse adipose-derived stem cells and to explore its effects on wound healing of full-thickness skin defects in rats.Methods:The research was an experimental research. Chitin nanofibers were prepared by acid hydrolysis and alkaline extraction method, and then mixed with hyaluronic acid and collagen to prepare chitin/hyaluronic acid/collagen hydrogels (hereinafter referred to as hydrogels). Besides, the hydrogels loaded with mouse adipose-derived stem cells were prepared. Thirty male 12-week-old guinea pigs were divided into negative control group, positive control group, and hydrogel group according to the random number table, with 10 guinea pigs in each group. Ethanol, 4-aminobenzoic acid ethyl ester, or the aforementioned prepared hydrogels without cells were topically applied on both sides of back of guinea pigs respectively for induced contact and stimulated contact, and skin edema and erythema formation were observed at 24 and 48 h after stimulated contact. Adipose-derived stem cells from mice were divided into normal control group cultured routinely and hydrogel group cultured with the aforementioned prepared hydrogels without cells. After 3 d of culture, protein expressions of platelet-derived growth factor-D (PDGF-D), insulin-like growth factor-Ⅰ (IGF-Ⅰ), and transforming growth factor β 1 (TGF-β 1) were detected by Western blotting ( n=3). Eight male 8-week-old Sprague-Dawley rats were taken and a circular full-thickness skin defect wound was created on each side of the back. The wounds were divided into blank control group without any treatment and hydrogel group with the aforementioned prepared hydrogels loaded with adipose-derived stem cells applied. Wound healing was observed at 0 (immediately), 2, 4, 8, and 10 d after injury, and the wound healing rate was calculated at 2, 4, 8, and 10 d after injury. Wound tissue samples at 10 d after injury were collected, the new tissue formation was observed by hematoxylin-eosin staining; the concentrations of interleukin-1α (IL-1α), IL-6, IL-4, and IL-10 were detected by enzyme-linked immunosorbent assay method; the expressions of CD16 and CD206 positive cells were observed by immunohistochemical staining and the percentages of positive cells were calculated. The sample numbers in animal experiment were all 8. Results:At 24 h after stimulated contact, no skin edema was observed in the three groups of guinea pigs, and only mild skin erythema was observed in 7 guinea pigs in positive control group. At 48 h after stimulated contact, skin erythema was observed in 8 guinea pigs and skin edema was observed in 4 guinea pigs in positive control group, while no obvious skin erythema or edema was observed in guinea pigs in the other two groups. After 3 d of culture, the protein expression levels of PDGF-D, IGF-I, and TGF-β 1 in adipose-derived stem cells in hydrogel group were significantly higher than those in normal control group (with t values of 12.91, 11.83, and 7.92, respectively, P<0.05). From 0 to 10 d after injury, the wound areas in both groups gradually decreased, and the wounds in hydrogel group were almost completely healed at 10 d after injury. At 4, 8, and 10 d after injury, the wound healing rates in hydrogel group were (38±4)%, (54±5)%, and (69±6)%, respectively, which were significantly higher than (21±6)%, (29±7)%, and (31±7)% in blank control group (with t values of 3.82, 3.97, and 4.05, respectively, Pvalues all <0.05). At 10 d after injury, compared with those in blank control group, the epidermis in wound in hydrogel group was more intact, and there were increases in hair follicles, blood vessels, and other skin appendages. At 10 d after injury, the concentrations of IL-1α and IL-6 in wound tissue in hydrogel group were significantly lower than those in blank control group (with tvalues of 8.21 and 7.99, respectively, P<0.05), while the concentrations of IL-4 and IL-10 were significantly higher than those in blank control group (with tvalues of 6.57 and 9.03, respectively, P<0.05). The percentage of CD16 positive cells in wound tissue in hydrogel group was significantly lower than that in blank control group ( t=8.02, P<0.05), while the percentage of CD206 positive cells was significantly higher than that in blank control group ( t=7.21, P<0.05). Conclusions:The hydrogel loaded with mouse adipose-derived stem cells is non-allergenic, can promote the secretion of growth factors in adipose-derived stem cells, promote the polarization of macrophages to M2 phenotype in wound tissue in rats with full-thickness skin defects, and alleviate inflammatory reaction, thereby promoting wound healing.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective We aimed to compare the efficacy and prognosis of percutaneous coronary intervention(PCI)in complex and high-risk patients with coronary heart disease(CHD)treated with extracorporeal membrane oxygenation(ECMO)combined with intra-aortic balloon pump(IABP)assistance,and explore the application value of combined use of mechanical circulatory support(MCS)devices in complex PCI.Methods A total of patients who met the inclusion criteria and underwent selective PCI supported by MCS at the Department of Cardiology,the First Affiliated Hospital of the Air Force Medical University from January 2018 to December 2022 were continuously enrolled.According to the mechanical circulatory support method,the patients were divided into ECMO+IABP group and IABP group.Clinical characteristics,angiographic features,in-hospital outcomes,and complications were collected.The intra-hospital outcomes and major adverse cardiovascular events(MACE)at one month and one year after the procedure were observed.The differences and independent risk factors between the two groups in the above indicators were analyzed.Results A total of 218 patients undergoing elective PCI were included,of which 66 patients were in the ECMO+IABP group and 152 patients were in the IABP group.The baseline characteristics of the two groups of patients were generally comparable,but the ECMO+IABP group had more complex lesion characteristics.The proportion of patients with atrial fibrillation(6.1％vs.0.7％,P=0.030),left main disease(43.9％vs.27.0％,P=0.018),triple vessel disease(90.9％vs.75.5％,P=0.009),and RCA chronic total occlusion disease(60.6％vs.35.5％,P＜0.001)was higher in the ECMO+IABP group compared to the IABP group.The proportion of patients with previous PCI history was higher in the IABP group(32.9％vs.16.7％,P=0.014).There was no statistically significant difference in the incidence of in-hospital complications between the two groups(P=0.176),but the incidence of hypotension after PCI was higher in the ECMO+IABP group(19.7％vs.9.2％,P=0.031).The rates of 1-month MACE(4.5％vs.2.6％,P=0.435)and 1-year MACE(7.6％vs.7.9％,P=0.936)were comparable between the two groups.Multivariate analysis showed that in-hospital cardiac arrest(OR 7.17,95％CI 1.27-40.38,P=0.025)and after procedure hypotension(OR 3.60,95％CI 1.10-11.83,P=0.035)were independent risk factors for the occurrence of 1-year MACE.Conclusions Combination use of ECMO+IABP support can provide complex and high-risk coronary heart disease patients with an opportunity to achieve coronary artery revascularization through PCI,and achieve satisfactory long-term prognosis.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

OBJECTIVE: To analyze Chinese medicine (CM) prescriptions for gastroesophageal reflux disease (GERD), we model topics on GERD-related classical CM literature, providing insights into the potential treatment. METHODS: Clinical guidelines were used to identify symptom terms for GERD, and CM literature from the database "Imedbooks" was retrieved for related prescriptions and their corresponding sources, indications, and other information. BERTopic was applied to identify the main topics and visualize the data. RESULTS: A total of 36,207 entries are queried and 1,938 valid entries were acquired after manually filtering. Eight topics were identified by BERTopic, including digestion function abate, stomach flu, respiratory-related symptoms, gastric dysfunction, regurgitation and gastrointestinal dysfunction in pediatric patients, vomiting, stroke and alcohol accumulation are associated with the risk of GERD, vomiting and its causes, regurgitation, epigastric pain, and symptoms of heartburn. CONCLUSIONS Topic modeling provides an unbiased analysis of classical CM literature on GERD in a time-efficient and scale-efficient manner. Based on this analysis, we present a range of treatment options for relieving symptoms, including herbal remedies and non-pharmacological interventions such as acupuncture and dietary therapy.
Humans ; Gastroesophageal Reflux/drug therapy* ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/therapeutic use* ; Models, Theoretical

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*