Efficacy of partial and complete resuscitative endovascular balloon occlusion of the aorta in the hemorrhagic shock model of liver injury.

BACKGROUND: Resuscitative endovascular balloon occlusion of the aorta (REBOA) can temporarily control traumatic bleeding. However, its prolonged use potentially leads to ischemia-reperfusion injury (IRI). Partial REBOA (pREBOA) can alleviate ischemic burden; however, its security and effectiveness prior to operative hemorrhage control remains unknown. Hence, we aimed to estimate the efficacy of pREBOA in a swine model of liver injury using an experimental sliding-chamber ballistic gun. METHODS: Twenty Landrace pigs were randomized into control (no aortic occlusion) (n=5), intervention with complete REBOA (cREBOA) (n=5), continuous pREBOA (C-pREBOA) (n=5), and sequential pREBOA (S-pREBOA) (n=5) groups. In the cREBOA and C-pREBOA groups, the balloon was inflated for 60 min. The hemodynamic and laboratory values were compared at various observation time points. Tissue samples immediately after animal euthanasia from the myocardium, liver, kidneys, and duodenum were collected for histological assessment using hematoxylin and eosin staining. RESULTS: Compared with the control group, the survival rate of the REBOA groups was prominently improved (all P<0.05). The total volume of blood loss was markedly lower in the cREBOA group (493.14±127.31 mL) compared with other groups (P<0.01). The pH was significantly lower at 180 min in the cREBOA and S-pREBOA groups (P<0.05). At 120 min, the S-pREBOA group showed higher alanine aminotransferase (P<0.05) but lower blood urea nitrogen compared with the cREBOA group (P<0.05). CONCLUSION: In this trauma model with liver injury, a 60-minute pREBOA resulted in improved survival rate and was effective in maintaining reliable aortic pressure, despite persistent hemorrhage. Extended tolerance time for aortic occlusion in Zone I for non-compressible torso hemorrhage was feasible with both continuous partial and sequential partial measures, and the significant improvement in the severity of acidosis and distal organ injury was observed in the sequential pREBOA.

MiR-17-5p-engineered sEVs Encapsulated in GelMA Hydrogel Facilitated Diabetic Wound Healing by Targeting PTEN and p21.

Delayed wound healing is a major complication of diabetes, and is associated with impaired cellular functions. Current treatments are unsatisfactory. Based on the previous reports on microRNA expression in small extracellular vesicles (sEVs), miR-17-5p-engineered sEVs (sEVs17-OE) and encapsulated them in gelatin methacryloyl (GelMA) hydrogel for diabetic wounds treatment are fabricated. SEVs17-OE are successfully fabricated with a 16-fold increase in miR-17-5p expression. SEVs17-OE inhibited senescence and promoted the proliferation, migration, and tube formation of high glucose-induced human umbilical vein endothelial cells (HG-HUVECs). Additionally, sEVs17-OE also performs a promotive effect on high glucose-induced human dermal fibroblasts (HG-HDFs). Mechanism analysis showed the expressions of p21 and phosphatase and tensin homolog (PTEN), as the target genes of miR-17-5p, are downregulated significantly by sEVs17-OE. Accordingly, the downstream genes and pathways of p21 and PTEN, are activated. Next, sEVs17-OE are loaded in GelMA hydrogel to fabricate a novel bioactive wound dressing and to evaluate their effects on diabetic wound healing. Gel-sEVs17-OE effectively accelerated wound healing by promoting angiogenesis and collagen deposition. The cellular mechanism may be associated with local cell proliferation. Therefore, a novel bioactive wound dressing by loading sEVs17-OE in GelMA hydrogel, offering an option for chronic wound management is successfully fabricated.

Identification of potential biomarkers and therapeutic targets for posttraumatic acute respiratory distress syndrome.

BACKGROUND: Despite improved supportive care, posttraumatic acute respiratory distress syndrome (ARDS) mortality has improved very little in recent years. Additionally, ARDS diagnosis is delayed or missed in many patients. We analyzed co-differentially expressed genes (co-DEGs) to explore the relationships between severe trauma and ARDS to reveal potential biomarkers and therapeutic targets for posttraumatic ARDS. METHODS: Two gene expression datasets (GSE64711 and GSE76293) were downloaded from the Gene Expression Omnibus. The GSE64711 dataset included a subset of 244 severely injured trauma patients and 21 healthy controls. GSE76293 specimens were collected from 12 patients with ARDS who were recruited from trauma intensive care units and 11 age- and sex-matched healthy volunteers. Trauma DEGs and ARDS DEGs were identified using the two datasets. Subsequently, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analyses were performed to elucidate the molecular functions of the DEGs. Then, hub genes of the co-DEGs were identified. Finally, to explore whether posttraumatic ARDS and septic ARDS are common targets, we included a third dataset (GSE100159) for corresponding verification. RESULTS: 90 genes were upregulated and 48 genes were downregulated in the two datasets and were therefore named co-DEGs. These co-DEGs were significantly involved in multiple inflammation-, immunity- and neutrophil activation-related biological processes. Ten co-upregulated hub genes (GAPDH, MMP8, HGF, MAPK14, LCN2, CD163, ENO1, CD44, ARG1 and GADD45A) and five co-downregulated hub genes (HERC5, IFIT2, IFIT3, RSAD2 and IFIT1) may be considered potential biomarkers and therapeutic targets for posttraumatic ARDS. Through the verification of the third dataset, posttraumatic ARDS may have its own unique targets worthy of further exploration. CONCLUSION: This exploratory analysis supports a relationship between trauma and ARDS pathophysiology, specifically in relationship to the identified hub genes. These data may serve as potential biomarkers and therapeutic targets for posttraumatic ARDS.

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties.

Hemostatic Effects of Bio-Zeolite Gauze and QuikClot Combat Gauze on Major Bleeding in Rabbits Acutely Exposed to High Altitude.

Objective: Hemostatic gauze application is an effective way to control major bleeding, which is the most common cause of death in trauma in both civilian and military settings. Coagulation derangement after acute exposure to high altitude might alter the effects of hemostatic gauzes. The present study aimed to observe the hemostatic effects of bio-zeolite gauze (BZG) and QuikClot Combat Gauze® (QCG) on major bleeding in rabbits acutely exposed to high altitude.Methods: Sixty rabbits were randomly and evenly divided into six groups. Animal models of simulated blast- and fragment-induced inguinal major bleeding were prepared in lower altitude and high-altitude areas, and BZG, QCG, and ordinary gauze without hemostatic material were used to control bleeding. The primary outcomes included immediate hemostasis rate, blood loss, and survival rate, while the secondary outcomes included hemodynamic parameters, laboratory examinations, and coagulation-relevant markers.Results: The overall effects of BZG and QCG were better than those of ordinary gauze, with a higher immediate hemostatic rate, less blood loss, and higher survival rate at 90 min after gauze application and higher red blood cell and platelet counts and lower creatinine level at 30 min after gauze application in lower altitude. The concentrations of coagulation factor XII and factor X in rabbits acutely exposed to high altitude were significantly lower than those in lower altitude. At high altitude, the hemostatic effects of BZG did not decrease significantly compared to those in the lower altitude, whereas those of ordinary gauze and QCG decreased significantly at high altitude compared to those in the lower altitude.Conclusions: Coagulation derangement after acute exposure to high altitude has negative effects on ordinary gauze and QCG but has no significant negative hemostatic effects on BZG.

Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma.

Objectives: Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods: Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results: Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708-0.820), 0.775 (95% CI: 0.728-0.823), and 0.756 (95% CI: 0.715-0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions: This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.

Nuclear paraspeckle assembly transcript 1 promotes the podocyte injury via targeting miR-23b-3p/B-cell lymphoma-2 interacting protein 3 like axis.

BACKGROUND: Given the reported effects of nuclear paraspeckle assembly transcript 1 (NEAT1) on kidney injury, a study is worth formulating to investigate whether and how NEAT1 impacts podocytes. MATERIALS AND METHODS: A mouse podocyte injury model was established using the adriamycin (ADR)-induced mouse podocyte cell line (MPC5). The target relationships between NEAT1 and microRNA (miR)-23b-3p and between miR-23b-3p and Bcl-2 interacting protein 3 like (BNIP3L) were verified by dual-luciferase reporter assay and RNA immunoprecipitation assay. After ADR-induced MPC5 cells were transfected with NEAT1 overexpression plasmid (oe-NEAT1) or shNEAT1, the viability and apoptosis of MPC5 cells were evaluated by Cell Counting Kit-8 (CCK-8) assay and flow cytometry, respectively. The expressions of MPC5, miR-23b-3p, BNIP3L and the factors related to podocyte injury, apoptosis and epithelial-mesenchymal transition were determined using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. RESULTS: NEAT1 was high-expressed in ADR-induced cell model. After transfection with oe-NEAT1, the expression of NEAT1, the levels of marker (Desmin) and apoptosis were promoted, while the viability and the levels of podocyte injury markers (WT1, Nephrin) were inhibited in ADR-induced cells. However, shNEAT1 generated the effects opposite to oe-NEAT1. Besides, miR-23b-3p competitively bound to NEAT1 and targeted BNIP3L. MiR-23b-3p inhibitor reversed the effect of shNEAT1, while its effect could be further offset by shBNIP3L. Furthermore, miR-23b-3p inhibitor affected mouse podocyte injury through downregulating Bcl-2 and E-cadherin levels and upregulating Cleaved-caspase-3, Bax, N-cadherin, Vimentin and Snail levels, but shBNIP3L did oppositely. CONCLUSION: NEAT1 promotes the podocyte injury via targeting miR-23b-3p/BNIP3L axis.

A prediction model for massive hemorrhage in trauma: a retrospective observational study.

BACKGROUND: Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma. METHODS: Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use. RESULTS: A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01-1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98-0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87-0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95-0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48-3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21-2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875-0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679-0.757). Model 1 was deployed as a public web tool ( http://82.156.217.249:8080/ ). CONCLUSIONS: Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.

[Establishment of a high-velocity fragment-induced penetrating liver injury model in landrace pigs].

OBJECTIVE: To establish a stable fragment-induced penetrating liver injury model in landrace pigs and evaluate the characteristics of deep tissue injury. METHODS: According to the different positioning methods of aiming points, twelve healthy adult landrace pigs were divided into group A (the relative height "h" of the aiming point and the highest point of the body surface on the tracing line was set to 5 cm) and group B ("h" was set to 6 cm). Ultrasonography was used to determine the direction of fragment projection, and an experimental ballistic gun was used to project high-velocity fragments to cause injury to animals. The vital signs of the two groups were monitored, and whole blood cell count, blood gas analysis, and liver and renal function were tested. Damages to the liver and adjacent organs, as well as the amount of bleeding and survival time were analyzed. RESULTS: For the overall analysis of the two groups, the liver hit rate of fragment simulating projectiles was 100% (right anterior lobe and right lateral lobe injury), the hit rate of other organs in the abdominal cavity was 25% (3/12), and the incidence of hemothorax or pneumothorax was 8% (1/12). The wounds were mainly characterized by liver lacerations, with total or partial disconnection of the distal liver lobe. There was no significant difference in wound length and bleeding amount between groups A and B [wound length (cm): 9.8±1.7 vs. 11.2±3.8, bleeding amount (g): 597.0±477.1 vs. 1 032.0±390.3, both P > 0.05]. The depth of liver parenchymal laceration in group B with the aiming point closer to the anterior median line was significantly longer than that in group A (cm: 2.8±0.4 vs. 1.9±0.6, P = 0.015). Mean arterial pressure (MAP), pH value, residual arterial blood base (BE), hemoglobin (Hb) and hematocrit (HCT) levels decreased after the fragment-induced injury, and then reached a trough level [MAP (mmHg, 1 mmHg ≈ 0.133 kPa): 87.0±33.6, pH: 7.26±0.15, BE (mmol/L): -6.65±8.48, Hb (g/L): 9.86±1.10, HCT: 0.309±0.029, all P < 0.05] in the first hour. Blood lactate (Lac), lactate dehydrogenase (LDH) and aspartate aminotransferase (AST) levels increased over time, and reached a peak level [Lac (mmol/L): 10.21±4.40, LDH (U/L): 1 417.0±223.3, AST (U/L): 234.5 (162.5, 357.5), both P < 0.05] at 1 hour after injury. Pearson's correlation analysis showed that the total amount of bleeding was correlated with the depth of liver parenchyma laceration (r = 0.684,P = 0.014). The Kaplan-Meier survival curve showed that the 3 hours survival rate in group A was higher than that in group B, but the difference was not statistically significant [83.3% (5/6) vs. 33.3% (2/6), P > 0.05]. CONCLUSIONS: The high-velocity fragment-induced penetrating liver injury model established by striking landrace pigs closer to the anterior median line with fragment simulating projectiles is reproducible and the degree of damage is controllable, and the model is applicable to further relevant research of hepatic ballistic trauma.

[Research on grading prediction model of traumatic hemorrhage volume based on deep learning].

OBJECTIVE: To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume. METHODS: A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system. RESULTS: Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95%CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95%CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95%CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN∗ model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95%CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO2), Total CO2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN∗ model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. CONCLUSIONS: Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Screening the Potential Biomarkers of COVID-19-Related Thrombosis Through Bioinformatics Analysis.

A high proportion of critically ill patients with coronavirus disease 2019 (COVID-19) experience thrombosis, and there is a strong correlation between anticoagulant therapy and the COVID-19 survival rate, indicating that common COVID-19 and thrombosis targets have potential therapeutic value for severe COVID-19.Gene expression profiling data were downloaded from Gene Expression Omnibus (GEO), and common differentially expressed genes (co-DEGs) were identified. The potential biological functions of these co-DEGs were explored by functional enrichment analysis, and protein-protein interaction (PPI) networks were constructed to elucidate the molecular mechanisms of the co-DEGs. Finally, hub genes in the co-DEG network were identified, and correlation analysis was performed.We identified 8320 upregulated genes and 7651 downregulated genes from blood samples of COVID-19 patients and 368 upregulated genes and 240 downregulated genes from blood samples of thrombosis patients. The enriched cellular component terms were mainly related to cytosolic ribosomes and ribosomal subunits. The enriched molecular function terms were mainly related to structural constituents of ribosomes and electron transfer activity. Construction of the PPI network and identification of hub genes ultimately confirmed that RPS7, IGF1R, DICER1, ERH, MCTS1, and TNPO1 were jointly upregulated hub genes, and FLNA and PXN were jointly downregulated hub genes.The identification of novel potential biomarkers provides new options for treating COVID-19-related thrombosis and reducing the rate of severe COVID-19.

A Bio-Inorganic Hybrid Hemostatic Gauze for Effective Control of Fatal Emergency Hemorrhage in "Platinum Ten Minutes".

Death from massive hemorrhage represents a global problem. It is a challenging task to design hemostatic materials with significant efficacy, good biocompatibility, reliable safety, and high stability. In this study, we demonstrate an effective bio-inorganic hybrid hemostat fabricated by stepwise procedures of on-site growth of zeolite gauze and immobilization of trypsin on the zeolite gauze. The as-synthesized hybrid hemostat catalyzes the transition of prothrombin-to-thrombin and exhibits excellent procoagulant performance in the both normal plasma and FX-deficient plasma. The hemostatic treatment of junctional femoral artery rupture in the porcine model confirms that this hybrid hemostat manifests itself with superior hemostatic performance over commercial hemostatic dressings, in terms of a reduced time to hemostasis and blood loss. The stability of the hybrid hemostat is validated through high temperature and violent shaking evaluation. This bio-inorganic hybrid hemostat displays high procoagulant activity, low cytotoxicity, and extended shelf life which may achieve the "Platinum Ten Minutes" rescue in battlefield and traffic accident medicine.

Avian influenza viruses suppress innate immunity by inducing trans-transcriptional readthrough via SSU72.

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can better escape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by which transcriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouse lungs, and patient PBMCs, we showed that genes on the complementary strand ("trans" genes) influenced by TRT were involved in the disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishment increased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 induced TRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT by reducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

ABSTRACT: Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

N6-Methylandenosine-Related lncRNAs in Tumor Microenvironment Are Potential Prognostic Biomarkers in Colon Cancer.

BACKGROUND: LncRNA dysregulation and the tumor microenvironment (TME) have been shown to play a vital role in the progression and prognosis of colon cancer (CC). We aim to reveal the potential molecular mechanism from the perspective of lncRNA in the TME and provide the candidate biomarkers for CC prognosis. METHODS: ESTIMATE analysis was used to divide the CC patients into high and low immune or stromal score groups. The expression array of lncRNA was re-annotated by Seqmap. Microenvironment-associated lncRNAs were filtered through differential analysis. The m6A-associated lncRNAs were screened by Pearson correlation analysis. Lasso Cox regression analyses were performed to construct the m6A- and tumor microenvironment-related lncRNA prognostic model (m6A-TME-LM). Survival analysis was used to assess the prognostic efficacy of candidate lncRNAs. Enrichment analyses annotated the candidate genes' functions. RESULTS: We obtained 25 common differentially expressed lncRNAs (DELs) associated with immune microenvironment and m6A-related genes for subsequent lasso analysis. Four out of these DELs were selected for the m6A-TME-LM. All the four lncRNAs were related to overall survival, and a test set testified the result. Further stratification analysis of the m6A-TME-LM retained its ability to predict OS for male and chemotherapy adjuvant patients and performed an excellent prognostic efficacy in the TNM stage III and IV subgroups. Network analysis also found the four lncRNAs mediated co-expression network was associated with tumor development. CONCLUSION: We constructed the m6A-TME-LM, which could provide a better prognostic prediction of CC.

Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends.

Temperature change between neighboring days (TCN) is an important trigger for cardiovascular diseases, but the modulated effects by seasonal temperature trends have been barely taken into account. A quantified comparison between impacts of positive TCNs (temperature rise) and negative situations (temperature drop) is also needed. We evaluated the associations of TCNs with emergency room (ER) visits for coronary heart disease (CHD) and cerebral infarction (CI) in Beijing, China, from 2008 to 2012. A year was divided into two segments dominated by opposite temperature trends, quasi-Poisson regression with distributed lag nonlinear models estimating TCN-morbidity relations were employed, separately for each period. High morbidities of CHD and CI both occurred in transitional seasons accompanied by large TCNs. Under warming backgrounds, positive TCNs increased CHD risk in patients younger than 65 years, and old people showed limited sensitivity. In the cooling periods, negative TCNs induced CHD risk in females and the elderly; the highest RR showed on lag 6 d. In particular, a same diurnal temperature decrease (e.g., - 2°C) induced greater RR (RR = 1.113, 95% CIs: 1.033-1.198) on old people during warming periods than cooling counterparts (RR = 1.055, 95% CIs: 1.011-1.100). Moreover, positive TCNs elevated CI risk regardless of background temperatures, and males were particularly vulnerable. Seasonal temperature trends modify TCN-cardiovascular morbidity associations significantly, which may provide new insights into the health impact of unstable weathers.

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

BACKGROUND: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator. METHODS: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission. RESULTS: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed. CONCLUSIONS: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

CT-US fusion imaging increases the feasibility of early ultrasound-guided percutaneous intervention of local drug therapy in pancreatic contusion and laceration.

BACKGROUND: Multimodal fusion imaging (MMFI) was usually used to assist percutaneous procedures for difficult lesions, with most applications occurring with hepatic and prostatic interventions. This paper aimed to evaluate the precision and effectiveness of computed tomography-ultrasound (CT-US) fusion imaging (CUFI)-assisted US-guided percutaneous intervention (UGPI) in early local drug therapy for pancreatic contusion and laceration (PCL). METHODS: A total of 12 pigs with PCL were randomly divided into a CUFI-assisted UGPI (MU) group (n=6) and a single UGPI (SU) group (n=6). The MU group underwent CUFI-assisted UGPI of locally applied medical protein glue (1 mL) injection while the SU group received the same therapy using two-dimensional UGPI. The duration and accuracy of each procedure were observed in the 2 groups. RESULTS: In the MU group, the overall time of the procedure for locking the plane was 1.85±0.06 minutes. Less time was spent in the selection of the pathway and puncture site in the MU group compared with the SU group (6.56±0.42 vs. 7.61±0.44 minutes, P<0.01). The duration of puncturing and drug injection was also shorter in the MU group than in the SU group (3.41±0.30 vs. 4.20±0.20 minutes, P<0.01) and the MU group had a higher accuracy of medical protein glue injection than the SU group (100% vs. 50%, P<0.05). CONCLUSIONS: CUFI could increase the precision and effectiveness of early UGPI in the delivery of local drug therapy in PCL.

Efficacy of New Zeolite-Based Hemostatic Gauze in a Gunshot Model of Junctional Femoral Artery Hemorrhage in Swine.

BACKGROUND: This work sought to (1) establish a reliable gunshot model of junctional femoral artery rupture in swine that accurately simulates field rescue conditions and (2) use the gunshot model to compare the efficacy and ease of application of zeolite nanometer hemostatic gauze with other hemostatic materials. METHODS: Thirty-six healthy landrace swine (body weight 50 ± 5 kg) were randomly divided into three groups which were treated with Combat Gauze (CG), FeiChuang hemostatic gauze (FG), or standard medical gauze (SG). A gunshot model of femoral artery hemorrhage in landrace swine was used with portable ultrasound to accurately position the wound. After the shooting, when mean arterial pressure of swine decreased by at least 30% for 10 s, wounds were pressed with standard packing (39 g) of gauze materials for 3 min to stop bleeding, then bandaged with pressure. Blood samples were taken 15 min before injury, then 10 min, 30 min, and 60 min after injury to determine hemodynamic, coagulation, and arterial blood gas indexes. Wound temperatures were taken at 5 min, 10 min, 30 min, and 60 min after injury, and survival times were recorded. The volume of blood loss and survival time were used to evaluate hemostatic effect, whereas the fill time, wound temperature, and physiological indexes were used to evaluate the safety and operation of the product. RESULTS: The CG (11.15 ± 3.09 mL/kg) and FG (12.19 ± 3.5 mL/kg) groups had significantly less blood loss than the SG group (16.8 ± 5.14 mL/kg) (P = 0.04; P = 0.039, respectively). After gauze packing, bleeding in CG (5.85 ± 1.17 mL/kg) and FG (5.37 ± 0.93 mL/kg) groups remained significantly lower than that of the SG group (6.93 ± 1.03 mL/kg) (P = 0.011; P = 0.003, respectively). Wound temperature rose with time for all groups (P < 0.001). The wound temperatures in the FG group and the CG group were significantly higher than that of the SG group (P = 004 and 0.009, respectively). Survival rates and times were not significantly different among the three groups, although the FG group had the longest average survival time (standard deviation [SD] 204.8 s), compared with the SG group (SD 177.8 s) and CG (SD 187.5 s) groups. No significant differences in hemodynamics, blood gas, and coagulation were observed among the three groups. CONCLUSIONS: The gunshot model of junctional femoral arterial hemorrhage guided by ultrasound had high accuracy for femoral arterial rupture by bullet wound and provided consistent and reproducible field-simulation conditions for comparison of hemostatic materials. FeiChuang zeolite hemostatic gauze effectively controlled bleeding as well as combat gauze, without excessive heat as found in other zeolite-based products. However, improvements to application technique, such as a packing device, are needed to improve operating time.

[Changes of arterial blood gas indexes of free-field primary blast lung injury of pigs and its application value].

OBJECTIVE: To observe the changes of arterial blood gas indexes in pigs with the free-field primary blast lung injury (PBLI) model, and to explore the value of arterial blood gas indexes in predicting moderate to severe PBLI. METHODS: Nine adult healthy Landrace pigs were selected to construct the pig free-field PBLI model. Arterial blood samples were taken 15 minutes before the explosion (before injury) and 10, 30, 60, 120, and 180 minutes after the explosion (after injury). Arterial blood gas indexes and pulse oxygen saturation (SpO2) were measured, compare the changes of blood gas analysis indexes and SpO2 levels at different time points, and observe the changes of gross injury scores and pathological injury scores of lung tissue. Analyze the correlation between the blood gas indicators. RESULTS: As time prolonged, at each time point, pH, arterial partial pressure of oxygen (PaO2), and SpO2 were lower than those before the injury, and blood lactic acid (Lac) and arterial partial pressure of carbon dioxide (PaCO2) were higher than those before the injury. Compared with that before the injury, the pH value in the blood decreased significantly 10 minutes after the injury (7.39±0.06 vs. 7.46±0.02, P < 0.05), and the Lac increased significantly (mmol/L: 3.61±2.89 vs. 1.10±0.28, P < 0.05), and lasts until 180 minutes after injury (pH value: 7.37±0.07 vs. 7.46±0.02, Lac (mmol/L): 2.40±0.79 vs. 1.10±0.28, both P < 0.05); while PaO2 and SpO2 decreased significantly at 180 minutes after injury [PaO2 (mmHg, 1 mmHg = 0.133 kPa): 59.40±10.94 vs. 74.81±9.39, P < 0.05; SpO2: 0.75±0.11 vs. 0.89±0.08, P < 0.05], PaCO2 increased significantly (mmHg: 56.17±5.38 vs. 48.42±4.93, P < 0.05). Correlation analysis showed that the gross injury score of lung blast injury animals was positively correlated with the pathological injury score (r = 0.866, P = 0.005); PaO2 and SpO2 were positively correlated (r = 0.703, P = 0.000); pH value and Lac were negative Correlation (r = -0.400, P = 0.006); pH value is negatively correlated with PaCO2 (r = -0.844, P = 0.000). CONCLUSIONS: This study successfully established a large mammalian free-field PBLI model, arterial blood gas analysis is helpful for the early diagnosis of PBLI, whether SpO2 can be used to evaluate the severity of lung injury remains to be further verified.