Placenta，fetal heart and brain affect each other in the process of fetal growth. They are influenced by genetic，environmental，epigenetic and hemodynamic factors，and share several key developmental pathways. Fetal heart defect in ongenital heart disease（CHD）is associated with abnormal development of placenta and brain. One-stop-shop ‘heart-brain-placenta’ imaging is of great value in prenatal diagnosis of CHD fetuses. This review discusses the current research on the one-stop-shop ‘heart-brain-placenta’ imaging of CHD fetuses.

Placenta，fetal heart and brain affect each other in the process of fetal growth. They are influenced by genetic，environmental，epigenetic and hemodynamic factors，and share several key developmental pathways. Fetal heart defect in ongenital heart disease（CHD）is associated with abnormal development of placenta and brain. One-stop-shop ‘heart-brain-placenta’ imaging is of great value in prenatal diagnosis of CHD fetuses. This review discusses the current research on the one-stop-shop ‘heart-brain-placenta’ imaging of CHD fetuses.

Objective:To conduct a visual analysis of the literature on burn-related coagulation dysfunction and to explore the current research status, evolution process, hot topics, and future research trends in burn-related coagulation dysfunction at home and abroad.Methods:The bibliometrics method was used. The literature on burn-related coagulation dysfunction which were published in Web of Science and China National Knowledge Internet databases from January 1, 1950 to May 1, 2022, and met the inclusion criteria were retrieved for publication volume analysis. The literature on burn-related coagulation dysfunction were retrieved as above in the core collection of Web of Science and China National Knowledge Internet databases, and CiteSpace 5.8.R3 software was used to perform co-occurrence analysis, cluster analysis, and literature co-citation analysis of key words. Results:A total of 501 and 235 literature on burn-related coagulation dysfunction were retrieved from Web of Science database and China National Knowledge Internet database, respectively. The literature on burn-related coagulation dysfunction emerged from 1975 and 1950, respectively, in China and abroad, which were gradually increased later. The frequency and centrality of Chinese key words such as 烧伤, 凝血功能, 血小板 were high in 235 literature in China National Knowledge Internet database, and the frequency and centrality of key words such as burn, coagulation, and deep vein thrombosis were high in 340 literature in the core collection of Web of Science database. In China National Knowledge Internet database, the top 6 Chinese key words in terms of burst intensity were 烧伤患者, 临床意义, 烧伤面积, 凝血功能, 预后, 血小板, and the first 3 among which were burst key words in the early stage; and in the core collection of Web of Science database, the key words with higher burst intensity were disseminated intravascular coagulation and pulmonary embolism, which were the burst key words in the early stage. The representative clustering labels in China National Knowledge Internet database were #0 烧伤, #1 休克, and #2 并发症, etc., and the representative clustering labels in the core collection of Web of Science database were #0 risk, #1 surgical patient, and #2 sepsis. Early researches in China National Knowledge Internet database and the core collection of Web of Science database focused on the presence of burn-related coagulation dysfunction itself, while the late researches focused on the relationship between burn-related coagulation dysfunction and inflammation, immunity, coagulation in general, and wounds. From 2010 onwards, there were a large number of core cited literature in the core collection of Web of Science database, and the prevention and treatment of vein thromboembolism was the most popular research direction in recent years. The researches on optimization and standardization of diagnostic methods and the overall mechanism of burn-related coagulation dysfunction would be the main research directions in the future. Conclusions:The research hotspots and evolution processes of burn-related coagulation dysfunction at home and abroad have both similarities and differences, and the current research hotspot is the relationship between coagulation and inflammation, immunity. With researches increasingly deepening, the researches on optimization and standardization of diagnostic methods and the overall mechanism of burn-related coagulation dysfunction will be the main research directions in the future.

Animals ; Antibodies, Neutralizing/biosynthesis* ; Antibodies, Viral/biosynthesis* ; Body Weight/immunology* ; COVID-19/virology* ; Disease Models, Animal ; Disease Progression ; Humans ; Immunohistochemistry ; Lung/virology* ; Male ; Mesocricetus ; Nasal Cavity/virology* ; RNA, Viral/immunology* ; SARS-CoV-2/pathogenicity* ; Severity of Illness Index ; Viral Load

Objective:To study the coagulation characteristics of adult patients with extensively severe burn in shock stage and its alarming value.Methods:Retrospective cohort study was performed on medical records of 37 adult patients with extensively severe burn who were admitted to the First Affiliated Hospital of Naval Medical University from January 2014 to December 2019 and met the inclusion criteria. The patients were divided into survival group ( n=23, 17 males and 6 females, aged 41 (31, 51) years) and death group ( n=14, 11 males and 3 females, aged 50 (43, 58) years) according to the prognosis of within 60 d after burn. Basic data of patients in the two groups and their routine coagulation indexes during shock period including prothrombin time (PT), thrombin time, activated partial thromboplastin time (APTT), D-Dimer, fibrinogen degradation product (FDP), fibrinogen, platelet, and international normalized ratio (INR) were recorded. Data were statistically analyzed with Wilcoxon rank sum test and Fisher′s exact probability test, prognosis-related factors was analyzed with single factor and multivariate logistic regression analysis (α selected=0.05, α excluded=0.1), and receiver operating characteristic (ROC) curve analysis were established to screen out the risk factors. All the patients were grouped into high score group and low score group according to the optimal threshold value, Kaplan-Meier method was used for survival analysis and Log-rank test was performed between the two groups. Results:Total burn surface area (TBSA) of patients in death group was obviously larger than that in survival group ( Z=2.980, P<0.01), while there were no statistically significant difference in the other indexes between the two groups ( P>0.05). Compared with those in survival group (16.10 (14.30, 16.90) s, 40.80 (36.20, 42.80) s, 1.30 (1.10, 1.40)), PT (18.70 (16.30, 22.70) s), APTT (46.45 (41.00, 57.10) s) and INR (1.55 (1.30, 1.96)) of patients in death group were significantly increased ( Z=2.540, 2.330, 2.300, P<0.05), there were no statistically significant difference in the other indexes between the two groups ( P>0.05). Single factor logistic regression analysis showed TBSA, PT, and APTT were factors related to death of adult patients with extensively severe burn within 60 d after burn (odds ratio (OR)=1.190, 1.214, 1.109, 95% confidence interval (CI)=1.053-1.346, 1.008-1.461, 1.012-1.215, P<0.05 or P<0.01). FDP and INR were potential factors related to death of adult patients with extensively severe burn within 60 d after burn (OR=1.040 and 4.559, 95% CI =0.998-1.083 and 0.918-22.641, P<0.1). Multivariate logistic stepwise regression was used to build models of APTT+ FDP+ TBSA and APTT+ FDP. Area under the curve (AUC) of APTT+ FDP+ TBSA model score was 0.944 (95% CI= 0.873-1.000), which was higher than AUC of APTT+ FDP model score (0.843, 95% CI=0.713-0.973) by ROC curve analysis. Optimal threshold value of APTT+ FDP+ TBSA model score was -0.879 4 with sensitivity of 100% (95% CI=100%-100%) and specificity of 87% (95% CI=74%-100%). Survival ratio of patients in high score group with optimal threshold value higher than -0.879 4 was significantly lower than that in low score group with optimal threshold value lower than -0.879 4, χ2=27.090, P<0.01. Conclusions:The coagulation state of adult patients with extensively severe burn in shock stage is characterized with procoagulant and hemostatic dysfunctions accompanied by enhanced fibrinolytic activity. The risk of death is significantly increased in adult patients with extensively severe burn with APTT+ FDP+ TBSA model score higher than -0.879 4.

Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ²=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ²=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ²=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ²=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.