Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Aim To predict the potential mechanism of ophiopogonin D (OPD) against pulmonary fibrosis by network pharmacology, and further verify it by experiment in vivo. Methods This study found that ophiopogon was the most frequently used drug in the treatment of pulmonary fibrosis with deficiency of Qi and Yin through data mining. In order to explore its material basis, network pharmacology analysis was carried out. A model of pulmonary fibrosis was established by bleomycin, and different concentrations of ophiopogonin D were administered to verify the results of the pharmacological network. Results Firstly, through network pharmacology analysis, it was found that mitophagy might be the potential target for ophiopogon to exert anti-pulmonary fibrosis effect. Meanwhile, network topology analysis showed that OPD had the greatest relationship with mitophagy. Animal experiments showed that OPD could relieve pulmonary fibrosis and reduce collagen deposition in mice. At the same time, the detection of mitophagy related proteins showed that the compound could increase the expression of PINK1 and Parkin proteins, reduce the content of P62 protein in lung tissue, and reduce the intracellular ROS level. Conclusions OPD can improve mitochondrial function and play an anti-pulmonary fibrosis role by promoting PINKl/Parkin dependent mitophagy in lung tissue.

SARS-CoV-2 infection causes complicated clinical manifestations with variable multi-organ injuries, however, the underlying mechanism, in particular immune responses in different organs, remains elusive. In this study, comprehensive transcriptomic alterations of 14 tissues from rhesus macaque infected with SARS-CoV-2 were analyzed. Compared to normal controls, SARS-CoV-2 infection resulted in dysregulation of genes involving diverse functions in various examined tissues/organs, with drastic transcriptomic changes in cerebral cortex and right ventricle. Intriguingly, cerebral cortex exhibited a hyperinflammatory state evidenced by significant upregulation of inflammation response-related genes. Meanwhile, expressions of coagulation, angiogenesis and fibrosis factors were also up-regulated in cerebral cortex. Based on our findings, neuropilin 1 (NRP1), a receptor of SARS-CoV-2, was significantly elevated in cerebral cortex post infection, accompanied by active immune response releasing inflammatory factors and signal transmission among tissues, which enhanced infection of the central nervous system (CNS) in a positive feedback way, leading to viral encephalitis. Overall, our study depicts a multi-tissue/organ transcriptomic landscapes of rhesus macaque with early infection of SARS-CoV-2, and provides important insights into the mechanistic basis for COVID-19-associated clinical complications.
Animals ; COVID-19/genetics* ; Macaca mulatta ; SARS-CoV-2/genetics* ; Transcriptome

Objective To investigate the pathogen distribution,imaging characteristics,and risk factors of pulmonary infection with multi-drug resistant organism (MDRO) in patients with severe craniocerebral injury,and establish and verify the risk prediction model. Methods A total of 230 patients with severe craniocerebral injury complicated with pulmonary infection were collected retrospectively.According to the 7∶3 ratio,they were randomly assigned into a modeling group (161 patients) and a validation group (69 patients).The risk factors of MDRO pulmonary infection were predicted with the data of the modeling group for the establishment of the risk prediction model.The data of the validation group was used to validate the performance of the model. Results Among the 230 patients,68 patients developed MDRO pulmonary infection.The isolated drug-resistant bacteria mainly included multi-drug resistant Acinetobacter baumannii,multi-drug resistant Klebsiella pneumoniae,multi-drug resistant Pseudomonas aeruginosa,and methicillin-resistant Staphylococcus aureus,which accounted for 45.21%,23.29%,16.44%,and 15.07%,respectively.The imaging characteristics included pleural effusion,lung consolidation,and ground-glass shadow,which accounted for 72.06%,63.24%,and 45.59%,respectively.Multivariate Logistic regression analysis showed that the independent risk factors for MDRO pulmonary infection included age ≥60 years (P=0.003),history of diabetes (P=0.021),history of chronic obstructive pulmonary disease (P=0.038),mechanical ventilation ≥7 d (P=0.001),transfer from other hospitals (P=0.008),and coma (P=0.002).A risk scoring model was established with the β value (rounded to the nearest integer) corresponding to each index in the regression equation.Specifically,the β values of age ≥60 years,history of diabetes,history of chronic obstructive pulmonary disease,mechanical ventilation ≥7 d,transfer from other hospitals,and coma were 1,1,1,2,2,and 1,respectively (value ≥4 indicated a high-risk population).The areas under the receiver operating characteristic curve of the modeling group and validation group were 0.845 and 0.809,respectively. Conclusions Multi-drug resistant Acinetobacter baumannii is the most common pathogen of MDRO pulmonary infection in patients with severe craniocerebral injury.Pleural effusion,lung consolidation,and ground-glass shadow were the most common imaging characteristics.The established risk model has high discriminant validity in both the modeling group and the validation group.
Coma ; Craniocerebral Trauma ; Drug Resistance, Multiple, Bacterial ; Humans ; Methicillin-Resistant Staphylococcus aureus ; Middle Aged ; Pleural Effusion ; Pneumonia ; Pulmonary Disease, Chronic Obstructive ; Retrospective Studies

ObjectiveTo explore the effect of Qishengwan on ileal flora during its treatment of Alzheimer's disease （AD） under the guidance of the theory of "interior-exterior relationship between heart and small intestine". MethodThe AD model was established by bilateral intraventricular injection of β-amyloid 1-42 （Aβ_1-42）. The rats were then randomly divided into the blank group， sham-operated group， model group， low-， medium-， and high-dose （5.6， 11.2，22.4 g·kg^-1·d^-1） Qishengwan groups， and donepezil （0.46 mg·kg^-1·d^-1） group. After medication for 28 successive days， the spatial memory ability of rats was observed in water maze test， and the levels of Aβ_1-42， nuclear transcription factor-κB （NF-κB）， tumor necrosis factor-α （TNF-α）， and interleukin-6 （IL-6） in the hippocampus were analyzed by enzyme-linked immunosorbent assay （ELISA）. Additionally， the contents of the ileum were collected and subjected to 16SrRNA-sequencing analysis for figuring out the changes in ileal flora. ResultCompared with the blank group and sham-operated group， the model group exhibited significantly reduced stay time in the target quadrant and number of target quadrant and platform crossings （P<0.05， P<0.01） and elevated Aβ_1-42 content in the hippocampus （P<0.01） and central inflammatory factors NF-κB， TNF-α， and IL-6 （P<0.05， P<0.01）. Compared with the model group， Qishengwan at each dose significantly alleviated the impaired spatial memory function （P<0.05， P<0.01）， improved the deposition of Aβ_1-42 in the hippocampus of rats （P<0.05， P<0.01）， and reduced the expression of central nervous system inflammatory factors （P<0.05， P<0.01）， thus exerting a good therapeutic effect on AD rats. The 16SrRNA-sequencing analysis results showed that the structure of the ileal flora in the model group was significantly separated from those in the blank group and sham-operated group. The abundance of Lachnospiraceae NK4A136 group was significantly increased （P<0.01）， while that of Escherichia-Shigella was reduced （P<0.05， P<0.01）. Qishengwan at each dose significantly changed the ileal flora structure and regulated the relative abundance of Lachnospiraceae NK4A136 group， Escherichia-Shigella， and Ruminococcaceae. ConclusionQishengwan has a positive therapeutic effect on AD. It can significantly enhance the memory and cognitive abilities in AD rats， which may be related to its regulation of the structure of rat ileal flora and the relative abundance of Lachnospiraceae NK4A136 group， Escherichia-Shigella， and Ruminococcaceae， the attenuation of the central neuroinflammatory response， and the reduction of central Aβ_1-42 deposition.

Based on the clinical characteristics of multiple sclerosis （MS） in traditional Chinese medicine （TCM） and western medicine and literature analysis， this paper aims to formulate the diagnostic criteria of TCM and western medicine for MS. Moreover， the modeling methods of experimental autoimmune encephalomyelitis （EAE）， animals for the modeling， and characteristics of the models were analyzed and summarized， and the consistency between the EAE models and the diagnostic criteria of TCM and western medicine was evaluated. The results showed that animal models had low consistency with the clinical characteristics in TCM （highest consistency 68%） and western medicine （highest consistency 60%）. Pathological models account for the majority of animal models for MS research， but there is a lack of intuitive performance indicators. Thus， it is difficult to comprehensively evaluate the models. The mental state， limb numbness， lack of strength， loss of muscle tone， tremor， and balance disorders of the mice are among the diagnostic criteria in western medicine. In TCM diagnostic criteria， the major symptoms which are reflected in animal behavior， such as physical fatigue， lack of strength， mental fatigue， distinclination to talk， and weak heavy numb limbs， are consistent with the western diagnostic criteria. The minor symptoms， including mental decline， bitter taste in mouth， frequent and urgent urination， fecal incontinence， and aggravated fever， are not well reflected in the models. According to TCM， MS is caused by deficiency of kidney essence and external contraction of pathogen， but no index is available for evaluating the external contraction of pathogen in existing animal models. The key to experimental research on MS is to establish an appropriate animal model based on the clinical pathogenesis and characteristics. However， there is a lack of MS animal model with TCM characteristics for syndrome classification. Therefore， renewed efforts should be made to prepare animal models with both TCM and western medicine characteristics that can be used in both basic experiments and clinical research.

OBJECTIVE: To evaluate the efficacy and safety of Pai-Neng-Da Capsule (, panaxadiol saponins component, PNDC) in combination with the cyclosporine and androgen for patients with chronic aplastic anemia (CAA). METHODS: A total of 79 CAA patients was randomly divided into 2 groups by a random number table, including PCA group [43 cases, orally PNDC 320 mg/d plus cyclosporine 5 mg/(kg·d) plus andriol 80 mg/d] and CA group [36 cases, orally cyclosporine 5 mg/(kg·d) plus andriol 160 mg/d]. All patients were treated and followed-up for 6 treatment courses over 24 weeks. The complete blood counts, score of Chinese medical (CM) symptoms were assessed and urine routine, electrocardiogram, hepatic and renal function were observed for safety evaluation. Female masculinization rating scale was established according to the actual clinical manifestations to evaluate the accurate degree of masculinization in female CAA patients treated by andriol. RESULTS: The effective rates were 88.1% (37/42) in the PCA group and 77.8% (28/36) in the CA group based on the standard for the therapeutic efficacy evaluation of hematopathy. There was no significant difference in the white blood cell (WBC) counts, platelet counts and hemoglobin concentration of peripheral blood between two groups after 6 months treatment. The masculinization score of female patient in the PCA group was significantly lower than the CA group (P<0.05). The mild abdominal distention was observed in 1 cases in the PCA group. In CA group, the abnormalities in the hepatic function developed in 2 cases and the renal disfunction was found in 1 case. CONCLUSION The PNDC possesses certain curative effects in the treatment of CAA without obvious side-effects and can partially replace andriol thereby to reduce the degree of masculinization [Registried at Chinese Clinical Trial Registry (ChicTR1900028153)].
Androgens ; Anemia, Aplastic/drug therapy* ; China ; Female ; Humans ; Nonprescription Drugs ; Saponins/therapeutic use*

OBJECTIVE: To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. METHODS: A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. RESULTS: Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. CONCLUSION The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.
Critical Illness ; Humans ; Intensive Care Units ; Machine Learning ; Patient Readmission ; ROC Curve

OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and to compare the prediction perfor-mance of the three machine learning models with the customized simplified acute physiology score Ⅱ(SAPS-Ⅱ) model. METHODS: We used medical information mart for intensive care (MIMIC)-Ⅲ database for model development and validation. The primary outcome was prolonged LOS-ICU(pLOS-ICU), defined as longer than the third quartile of patients' LOS-ICU in the studied dataset. The recursive feature elimination method was used to do feature selection for three machine learning models. We utilized 5-fold cross validation to evaluate model prediction performance. The Brier value, area under the receiver operation characteristic curve (AUROC), and estimated calibration index (ECI) were used as perfor-mance measures. Performances of the four models were compared, and performance differences between the models were assessed using two-sided t test. The model with the best prediction performance was employed to generate variable importance ranking, and the identified top five important predictors were pre-sented. RESULTS: The final cohort in our study consisted of 40 200 eligible ICU patients, of whom 23.7% were with pLOS-ICU. The proportion of the male patients was 57.6%, and the age of all the ICU patients was (61.9±16.5) years.Results showed that the three machine learning models outperformed the customized SAPS-Ⅱ model in terms of all the performance measures with statistical significance (P < 0.01). Among the three machine learning models, the RF model achieved the best overall performance (Brier value, 0.145), discrimination (AUROC, 0.770) and calibration (ECI, 7.259). The calibration curve showed that the RF model slightly overestimated the risk of pLOS-ICU in high-risk ICU patients, but underestimated the risk of pLOS-ICU in low-risk ICU patients. Top five important predictors for pLOS-ICU identified by the RF model included age, heart rate, systolic blood pressure, body tempe-rature, and ratio of arterial oxygen tension to the fraction of inspired oxygen(PaO₂/FiO₂). CONCLUSION The RF algorithm-based pLOS-ICU prediction model had a best prediction performance in this study. It lays a foundation for future application of the RF-based pLOS-ICU prediction model in ICU clinical practice.
Aged ; Humans ; Intensive Care Units ; Machine Learning ; Male ; Middle Aged ; Research Design

OBJECTIVE: This study tests whether long-term intake of Allium tuberosum (AT) can alleviate pulmonary inflammation in ovalbumin (OVA)-induced asthmatic mice and evaluates its effect on the intestinal microbiota and innate lymphoid cells (ILCs). METHODS: BALB/c mice were divided into three groups: phosphate buffer saline, OVA and OVA + AT. The asthmatic murine model was established by sensitization and challenge of OVA in the OVA and OVA + AT groups. AT was given to the OVA + AT group by oral gavage from day 0 to day 27. On day 28, mice were sacrificed. Histopathological evaluation of lung tissue was performed using hematoxylin and eosin, and periodic acid-Schiff staining. The levels of IgE in serum, interleukin-5 (IL-5) and IL-13 from bronchoalveolar lavage fluid (BALF) were measured by enzyme-linked immunosorbent assay. The ILCs from the lung and gut were detected by flow cytometry. 16S ribosomal DNA sequencing was used to analyze the differences in colon microbiota among treatment groups. RESULTS: We found that long-term intake of AT decreased the number of inflammatory cells from BALF, reduced the levels of IL-5 and IL-13 in BALF, and IgE level in serum, and rescued pulmonary histopathology with less mucus secretion in asthmatic mice. 16S ribosomal DNA sequencing results showed that AT strongly affected the colonic bacteria community structure in asthmatic mice, although it had no significant effect on the abundance and diversity of the microbiota. Ruminococcaceae and Desulfovibrionaceae were identified as two biomarkers of the treatment effect of AT. Moreover, AT decreased the numbers of ILCs in both the lung and gut of asthmatic mice. CONCLUSION The results indicate that AT inhibits pulmonary inflammation, possibly by impeding the activation of ILCs and adjusting the homeostasis of gut microbiota in asthmatic mice.