Objective: To investigate the effects of blood component transfusion on the prognosis of patients with varying severity of traumatic brain injury (TBI). Methods: A retrospective analysis was conducted on clinical data from 621 TBI patients admitted between January 2012 and December 2022. The patients in the blood transfusion group were categorized into three groups based on Glasgow Coma Scale (GCS) scores: severe impairment (GCS 3-8, n=302), moderate impairment (GCS 9-12, n=186), and mild impairment (GCS 13-14, n=133). General clinical data and laboratory test indexes were analyzed. Patients were further divided into two subgroups based on in-hospital mortality: death group (n=72) vs survival group (n=549). Univariate and multivariate logistic regression analysis was used to analyze the effects of different blood component transfusion volumes on the prognosis of TBI patients. ROC curve was used to evaluate the prognostic value of red blood cell transfusion volume. Results: Patients with GCS scores 3-8 had significantly longer hospital stays (21.73±15.89 vs 20.83±11.54 vs 15.5±7.76) and higher RBC transfusion volumes (6.16±6.79 vs 4.67±2.81 vs 3.67±3.20) than the other two groups (P<0.05). NLR, PCT, CRP, PT, Fib, FDP and DDI after the last transfusion showed significant differences from pre-transfusion values (P<0.05). The death group exhibited higher transfusion volumes of RBCs, plasma, platelets, and cryoprecipitate compared with the survival group (P<0.05). Univariate (OR: 1.541, 95%CI: 1.412-1.682) and multivariate (OR: 1.522, 95%CI: 1.362-1.700) logistic regression analyses showed that the RBC transfusion volume was a risk factor affecting the prognostic factors of TBI patients after infusion of blood components. ROC curve analysis showed that RBC transfusion volume could serve as a prognostic marker (sensitivity: 0.708, specificity: 0.812). Conclusion: Blood component transfusion alters inflammatory and coagulation markers in patients with different degrees of TBI, and RBC transfusion volume is a viable prognostic indicator for TBI outcomes.

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

Objective:To investigate the early clinical characteristics of elderly patients with severe burns and the risk factors on prognosis.Methods:This study was a retrospective case series study. Clinical data of 124 elderly patients with severe burns who met the inclusion criteria and were admitted to the 12 hospitals from January 2015 to December 2020 were collected, including 4 patients from the Fourth People's Hospital of Dalian, 5 patients from Fujian Medical University Union Hospital, 22 patients from Guangzhou Red Cross Hospital of Jinan University, 5 patients from Heilongjiang Provincial Hospital, 27 patients from the First Affiliated Hospital of Naval Medical University, 9 patients from the First Affiliated Hospital of Nanchang University, 10 patients from Affiliated Hospital of Nantong University, 9 patients from Tongren Hospital of Wuhan University & Wuhan Third Hospital, 12 patients from the 924 th Hospital of PLA, 6 patients from Zhangjiagang First People's Hospital, 4 patients from Taizhou Hospital of Zhejiang Province, and 11 patients from Zhengzhou First People's Hospital. The patients' overall clinical characteristics, such as gender, age, body mass index, total burn area, full-thickness burn area, inhalation injury, causative factors, whether combined with underlying medical diseases, and admission time after injury were recorded. According to the survival outcome within 28 days after injury, the patients were divided into survival group (89 cases) and death group (35 cases). The following data of patients were compared between the two groups, including the basic data and injuries (the same as the overall clinical characteristics ahead); the coagulation indexes within the first 24 hours of injury such as prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen degradation product (FDP), international normalized ratio (INR), and fibrinogen; the blood routine indexes within the first 24 hours of injury such as white blood cell count, platelet count, neutrophil-to-lymphocyte ratio, monocyte count, red blood cell count, hemoglobin, and hematocrit; the organ function indexes within the first 24 hours of injury such as direct bilirubin, total bilirubin, urea, serum creatinine, aspartate aminotransferase, alanine aminotransferase, total protein, albumin, globulin, blood glucose, triglyceride, total cholesterol, alkaline phosphatase, creatine kinase, electrolyte indexes (potassium, sodium, chlorine, calcium, magnesium, and phosphorus in blood), uric acid, myoglobin, and brain natriuretic peptide; the infection and blood gas indexes within the first 24 hours of injury such as procalcitonin, C-reactive protein, pH value, oxygenation index, base excess, and lactate; treatment such as whether conducted with mechanical ventilation, whether conducted with continuous renal replacement therapy, whether conducted with anticoagulation therapy, whether applied with vasoactive drugs, and fluid resuscitation. The analysis was conducted to screen the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns. Results:Among 124 patients, there were 82 males and 42 females, aged 60-97 years, with body mass index of 23.44 (21.09, 25.95) kg/m 2, total burn area of 54.00% (42.00%, 75.00%) total body surface area (TBSA), and full-thickness burn area of 25.00% (10.00%, 40.00%) TBSA. The patients were mainly combined with moderate to severe inhalation injury and caused by flame burns. There were 43 cases with underlying medical diseases. The majority of patients were admitted to the hospital within 8 hours after injury. There were statistically significant differences between patients in the 2 groups in terms of age, total burn area, full-thickness burn area, and inhalation injury, and PT, APTT, D-dimer, FDP, INR, white blood cell count, platelet count, urea, serum creatinine, blood glucose, blood sodium, uric acid, myoglobin, and urine volume within the first 24 hours of injury (with Z values of 2.37, 5.49, 5.26, 5.97, 2.18, 1.95, 2.68, 2.68, 2.51, 2.82, 2.14, 3.40, 5.31, 3.41, 2.35, 3.81, 2.16, and -3.82, respectively, P<0.05); there were statistically significant differences between two groups of patients in whether conducted with mechanical ventilation and whether applied with vasoactive drugs (with χ2 values of 9.44 and 28.50, respectively, P<0.05). Age, total burn area, full-thickness burn area, serum creatinine within the first 24 hours of injury, and APTT within the first 24 hours of injury were the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns (with odds ratios of 1.17, 1.10, 1.10, 1.09, and 1.27, 95% confidence intervals of 1.03-1.40, 1.04-1.21, 1.05-1.19, 1.05-1.17, and 1.07-1.69, respectively, P<0.05). Conclusions:The elderly patients with severe burns had the injuries mainly from flame burns, often accompanied by moderate to severe inhalation injury and enhanced inflammatory response, elevated blood glucose levels, activated fibrinolysis, and impaired organ function in the early stage, which are associated with their prognosis. Age, total burn area, full-thickness burn area, and serum creatinine and APTT within the first 24 hours of injury are the independent risk factors for death within 28 days after injury in this population.

Objective:To systematically evaluate the effect of different durations of prone ventilation on the efficacy of patients with acute respiratory distress syndrome (ARDS).Methods:A computer search was conducted in databases including PubMed, Cochrane Library, Embase, CNKI, Wanfang Database, VIP Database, and China Biomedical Literature Database for studies on prone ventilation for the treatment of adult patients with ARDS published from the establishment of the database to September 2023. Studies were categorized into ≤24 hours group and > 24 hours group based on the duration of prone ventilation. Outcome indicators included mortality, the length of intensive care unit (ICU) stay, incidence of pressure ulcers, and operation of tracheotomy. Two researchers independently screened the literature, extracted information, and evaluated the risk of bias of the included literature. The quality of the included literature was assessed using the NOS scale, and the effect of different durations of prone ventilation on the efficacy of ARDS was analyzed by Meta-analysis.Results:A total of 517 patients from 4 papers were finally included, including 249 patients with prone ventilation duration ≤24 hours and 268 patients with prone ventilation duration > 24 hours. All 4 studies were cohort studies, and the overall inclusion of literature assessed for methodological quality indicated high study quality and low risk of bias. Meta-analysis showed that there were no significantly differences in mortality [relative risk ( RR) = 1.02, 95% confidence interval (95% CI) was 0.79 to 1.31, P = 0.88], the length of ICU stay [mean difference ( MD) = -2.68, 95% CI was -5.30 to - 0.05, P = 0.05] between the prone ventilation duration ≤ 24 hours group and prone ventilation duration > 24 hours group. Compared with the prone ventilation duration ≤24 hours group, the incidence of pressure ulcers ( RR = 0.76, 95% CI was 0.59 to 0.98, P = 0.04) and the operation of tracheotomy ( RR = 0.71, 95% CI was 0.53 to 0.94, P = 0.02) were significantly increased in the prone ventilation duration > 24 hours group. Conclusions:The duration of prone ventilation had no significant effect on the mortality and the length of ICU stay in ARDS patients, but prone ventilation for > 24 hours increased the incidence of pressure ulcers and the operation of tracheotomy, which still needs to be further verified by a large number of studies due to the small number of included studies.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Stroma surrounding the tumor cells plays crucial roles for tumor progression. However, little is known about the factors that maintain the symbiosis between stroma and tumor cells. In this study, we found that the transcriptional regulator-signal transducer and activator of transcription 3 (Stat3) was frequently activated in cancer-associated fibroblasts (CAFs), which was a potent facilitator of tumor malignancy, and formed forward feedback loop with platelet-activating factor receptor (PAFR) both in CAFs and tumor cells. Importantly, PAFR/Stat3 axis connected intercellular signaling crosstalk between CAFs and cancer cells and drove mutual transcriptional programming of these two types of cells. Two central Stat3-related cytokine signaling molecules-interleukin 6 (IL-6) and IL-11 played the critical role in the process of PAFR/Stat3 axis-mediated communication between tumor and CAFs. Pharmacological inhibition of PAFR and Stat3 activities effectively reduced tumor progression using CAFs/tumor co-culture xenograft model. Our study reveals that PAFR/Stat3 axis enhances the interaction between tumor and its associated stroma and suggests that targeting this axis can be an effective therapeutic strategy against tumor malignancy.

Among current novel druggable targets, protein–protein interactions (PPIs) are of considerable and growing interest. Diacylglycerol kinase α (DGKα) interacts with focal adhesion kinase (FAK) band 4.1-ezrin-radixin-moesin (FERM) domain to induce the phosphorylation of FAK Tyr397 site and promotes the malignant progression of esophageal squamous cell carcinoma (ESCC) cells. Chrysin is a multi-functional bioactive flavonoid, and possesses potential anticancer activity, whereas little is known about the anticancer activity and exact molecular mechanisms of chrysin in ESCC treatment. In this study, we found that chrysin significantly disrupted the DGKα/FAK signalosome to inhibit FAK-controlled signaling pathways and the malignant progression of ESCC cells both in vitro and in vivo, whereas produced no toxicity to the normal cells. Molecular validation specifically demonstrated that Asp435 site in the catalytic domain of DGKα contributed to chrysin-mediated inhibition of the assembly of DGKα/FAK complex. This study has illustrated DGKα/FAK complex as a target of chrysin for the first time, and provided a direction for the development of natural products-derived PPIs inhibitors in tumor treatment.

【Objective】 To analyze the effect of blood component transfusion when the results of direct antiglobulin test (DAT) changed from negative to positive after blood transfusion. 【Methods】 The data of 215 surgical blood recipients, who were admitted in our hospital from January to October 2019 and presented negative results for both DAT and irregular antibody screening (Anti-screening), were collected via Ruimei Laboratory Management System. DAT and Anti-screening were performed again after blood transfusion, and DAT positive patients(re-test positive group) were then subject to antibody classification and polybrene cross-matching (referred to as cross-matching), and Anti-screening positive patients were tested for irregular antibodies. Patients were stratified by perioperative RBCs transfusion volume as ≤4 U (150 ± 10% mL/U), >4 to 8 U and > 8 U, and DAT-negative patients after blood transfusion were set as the controls, and the transfusion effect of DAT-positive patients after blood transfusion was compared with them. 【Results】 8.84% (19/215) of DAT-negative patients turned positive after RBCs transfusion, among which IgG type accounted for 84.21% (16/19) and IgG+ C3 15.79% ( 3/19); two patients(anti-E and-M, 10.53%) were positive in anti-screening re-test and the rest were negative (89.47%, 17/19). As for cross matching, incompatibility of both primary and secondary side, primary side and secondary side accounted for 5.26% (1/19), 5.26% (1/19) and 10.52 (2/19), respectively, while 78.95% (15/19) showed compatibility of both primary and secondary side. The Hb, RBC and Hct values of the re-test positive group, received RBC transfusion volume (U)≤4 and >4~8, were effectively elevated compared with the controls (P<0.05), while no significant changes of the parameters were noticed when blood transfusion volume >8 U(P>0.05). 【Conclusion】 The conversion of DAT negative results to positive after RBC transfusion indicates the patient has developed antibodies or the incidence of blood transfusion reaction, which can provide references for the clinical choice of appropriate blood components to ensure the safety and effectiveness of blood transfusion.

Objective:To know the critical care resources of the different class-hospitals in Guizhou Province, China, and to provide the direction and evidence for quality improvement and discipline construction of critical care medicine in Guizhou Province.Methods:The resource status of the departments of intensive care unit (ICU) in Guizhou Province was obtained through form filling and/or field investigation. The forms were filled and submitted from May 2017 to February 2018, and the field investigation (some of the hospitals) was carried out in March 2018. The data of hospitals in Guizhou Province in 2018, was obtained from the official website of Health Committee of Guizhou Province, which was released online on November 28th, 2019. The obtained data were summarized and analyzed according to different aspects such asthe status of ICU construction, main equipment configuration and technology implementation.Results:There were 39 third-class hospitals and 77 second-class hospitals included in this study, which accounted for 76.5% (39/51) of third-class public hospitals and 50.0% (77/154) of second-class public hospitals respectively. Among them, there were 86.8% (33/38) of third-class general hospitals and 50.4% (69/137) of second-class general hospitals respectively. In terms of ICU construction, compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals were established earlier [years: 2011 (2008, 2012) vs. 2013 (2011, 2015), P < 0.01], had more ICU beds, doctors and nurses [15 (11, 20) vs. 8 (6, 10), 9 (8, 11) vs. 6 (5, 7), 25 (20, 41) vs. 15 (12, 19), respectively, all P < 0.01]. However, there were no significant differences regarding the doctor-bed ratio and the nurse-bed ratio in ICUs between second-class hospitals and third-class hospitals. In terms of main equipment configuration, compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals had more ventilators, higher ratio of ventilators to beds, more infusion pumps, higher ratio of infusion pumps to beds, more monitor, gastrointestinal nutrition pumps and single rooms, and higher proportion of ICUs equipped with negative pressure rooms [ventilators: 14 (10, 18) vs. 6 (4, 8), ratio of ventilators to beds: 1.0 (0.7, 1.1) vs. 0.8 (0.6, 1.0), infusion pumps: 10 (6, 20) vs. 5 (3, 8), ratio of infusion pumps to beds: 0.8 (0.0, 1.0) vs. 0.0 (0.0, 0.4), monitor: 18 (13, 24) vs. 9 (6, 12), gastrointestinal nutrition pumps: 2 (1, 5) vs. 1 (0, 3), single rooms: 2 (1, 3) vs. 1 (0, 3), proportion of ICUs equipped with negative pressure rooms: 53.8% (21/39) vs. 31.5% (23/73), respectively, all P < 0.05]. Furthermore, there were higher proportions of ICUs equipped with portable ventilator, pulse indicator continuous cardiac output monitoring (PiCCO), intra-aortic balloon pump (IABP), extra-corporeal membrane oxygenation (ECMO), B ultrasound machine, bronchoscope, pressure of end-tidal carbondioxide (P ETCO 2) monitoring, bispectral index (BIS) monitoring, bedside gastroscopy, the apparatus used for the prevention of deep vein thrombosis of lower extremity in third-class hospitals than in second-class hospitals [portable ventilator: 86.7% (26/30) vs. 59.6% (28/47), 43.3% (13/30) vs. 1.5% (1/66), 14.3% (4/28) vs. 0% (0/65), 10.7% (3/28) vs. 0% (0/65), 62.5% (20/32) vs. 37.3% (25/67), 97.1% (33/34) vs. 63.6% (42/66), 60.6% (20/33) vs. 28.4% (19/67), 17.2% (5/29) vs. 0% (0/65), 27.6% (8/29) vs. 1.5% (1/65), 77.4% (24/31) vs. 52.3% (34/65), respectively, all P < 0.05]. In terms of skills development, there were more ICUs carried out intracranial pressure monitoring, abdominal pressure monitoring, ultrasound diagnosis, bronchoscope examination and treatment and blood purification in third-class hospitals than in second-class hospitals [31.6% (12/38) vs. 14.7% (11/75), 75.7% (28/37) vs. 38.6% (27/70), 61.5% (24/39) vs. 24.3% (18/74), 89.7% (35/39) vs. 45.9% (34/74), 92.3% (36/39) vs. 48.6% (36/74), respectively, all P < 0.05]. Conclusions:The data were mainly derived from public general hospitals in Guizhou Province. Compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals were founded earlier and larger, had better hardware configuration and could carry out more skills. However, the human resource situations were similar between second-class hospitals and third-class hospitals. Both second-class hospitals and third-class hospitals have a need to improve the allocation of manpower and equipment and expand various skills in ICUs, while it is more urgent for second-class hospitals.

Objective:To discuss the feasibility of offering specialized courses of critical care medicine in undergraduate clinical medicine education, so as to alleviate the shortage of critical care medicine staffs and lay a foundation for improving the success rate for the treatment of critical cases.Methods:The undergraduates majoring in clinical medicine from 2008 to 2011 in Guizhou Medical University (the former Guiyang Medical College) were enrolled. After they had been enrolled in the undergraduate education for 3 years and were ready for Grade four, which meant basic medicine teaching had been completed and clinical medicine teaching was about to start, they were introduced and preached to each discipline, including critical care medicine. The undergraduates were free to choose professional direction of clinical training in Grade four. Students majoring in clinical medicine from 2012 to 2014 were free to choose their major direction when they entered the school.Results:From September 2011 to July 2019, the university had cultivated 246 undergraduates majoring in clinical critical care medicine from 2008 to 2014, and the critical care medicine professional team of affiliated hospital had undertaken 540 teaching hours. By July 2019, all students had graduated on time, with an employment rate of 100%. Forty students took postgraduate programs in our school and other schools, accounting for 16.3%.Conclusions:Professional education of critical care medicine in the undergraduate course of clinical medicine can mobilize students' interest in learning and subjective initiative, which is conducive to career selection. During the clinical training, the students can identify and timely cure critical care cases in the early stage, and partly alleviate the current shortage of critical care medical staffs.