Effects of dual-gene modification on biological characteristics of vascular endothelial cells and their significance as reserving cells for chronic wound repair.

bFGF is a commonly used and reliable factor for improving chronic wound healing, and hSulf-1 expression is abundant in surrounding cells of chronic wound tissue and vascular endothelial cells, which can reverse the effect of bFGF and inhibit the signalling activity of cell proliferation. In this study, an adenovirus, Ad5F35ET1-bFGF-shSulf1, was designed for establishing the dual-gene modified vascular endothelial cells, which were used as the repair cells for skin chronic wound. Ad5F35ET1-bFGF-shSulf1 infected ECV304 cells in vitro and mediated the overexpression of bFGF and the knockdown of hSulf-1, which effectively activated the AKT and ERK signal transduction pathways, facilitate cell proliferation and migration, with the cell viability to 128.29% at 72 h after infection, compared to 66.65%, 73.74%, 87.63%, 103.14% in the blank control, Ad5F35ET1-EGFP-shNC, Ad5F35ET1-shSulf1, Ad5F35ET1-bFGF groups, respectively. In the rat ear skin injury model, the wound healing was significantly accelerated in the Ad5F35ET1-rbFGF-shrSulf1 group compared to the blank control group (p = 0.0046), Ad5F35ET1-EGFP-shNC group (p = 0.0245), Ad5F35ET1-shrSulf group (p = 0.0426), and Ad5F35ET1-rbFGF group (p = 0.2853). The results demonstrated that this strategy may be a candidate therapy for chronic injury repair.

[Development of acute kidney injury prognostic model for critically ill patients based on MIMIC-III database].

OBJECTIVE: To investigate the risk factors affecting the prognosis of patients with acute kidney injury (AKI) in the intensive care unit (ICU) based on the Medical Information Mart for Intensive Care III (MIMIC-III) database, and to establish a prognostic model for AKI. METHODS: Patients (aged ≥ 18 years) with acute renal failure, admitted to the ICU for the first time, and had complete hospital records (the RIFLE diagnostic criteria were used in the database, and the diagnosis was expressed as AKI in this article) were screened from MIMIC-III database according to diagnostic codes. Patients were divided into two groups based on survival state at discharge, and the general information, underlying diseases, injury factors, vital signs and laboratory indicators within 24 hours after AKI, related intervention and prognostic indicators were analyzed. Univariate and multivariate Logistic regression analysis were used to determine the risk factors affecting mortality in patients with AKI and established a prediction model. The receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the prediction model for the prognosis of AKI patients. RESULTS: There were 4 554 patients with AKI included and 862 died, with mortality of 18.93%. Univariate Logistic regression analysis was performed for factors that might be associated with death in AKI patients, and the results showed that age, hypertension, lymphoma, metastatic carcinoma, vancomycin, aspirin, coagulation abnormalities, cardiac arrest, sepsis or septic shock, invasive mechanical ventilation, white blood cell count (WBC), platelet count (PLT), K+, blood urea nitrogen (BUN), total bilirubin (TBil), renal replacement therapy (RRT) and length of stay (LOS) were independent risk factors [odds ratio (OR) and 95% confidence interval (95%CI) were 1.002 (1.001-1.003), 0.764 (0.618-0.819), 1.749 (1.112-2.752), 2.606 (1.968-3.451), 1.779 (1.529-2.071), 0.689 (0.563-0.842), 1.871 (1.590-2.201), 2.468 (1.209-5.036), 2.610 (2.226-3.060), 2.154 (1.853-2.505), 1.105 (1.009-1.021), 0.998 (0.997-0.998), 1.132 (1.057-1.212), 1.008 (1.006-1.011), 1.061 (1.049-1.073), 2.142 (1.793-2.997), 0.805 (0.778-1.113), all P < 0.05]. Further binary Logistic regression analysis showed that lymphoma, metastatic cancer, vancomycin, cardiac arrest, sepsis or septic shock, coagulation dysfunction, invasive mechanical ventilation, increased BUN, increased TBil, increased or decreased blood K+ and increased WBC were independent risk factors for death [ß values were 0.636, 1.005, 0.207, 0.894, 0.787, 0.346, 0.686, 0.006, 0.051, 0.085, and 0.009; OR and 95%CI were 1.889 (1.177-3.031), 2.733 (2.027-3.683), 1.229 (1.040-1.453), 2.445 (1.165-5.133), 2.197 (1.850-2.610), 1.413 (1.183-1.689), 1.987 (1.688-2.338), 1.006 (1.003-1.009), 1.052 (1.039-1.065), 1.089 (1.008-1.176), and 1.009 (1.004-1.015), respectively, all P < 0.05]. The Hosmer-Lemeshow test showed that the AKI prognostic model was able to fit the observed data well (P = 0.604). ROC curve analysis showed that the area under ROC curve (AUC) of the AKI prognostic model was 0.716 (95%CI was 0.697-0.735), when the cut-off value was 0.320, the sensitivity was 71.9%, the specificity was 60.1%, the positive likelihood ratio was 1.80, and the negative likelihood ratio was 0.47. CONCLUSIONS: The prognostic prediction model of AKI in critically ill patients established and based on the MIMIC-III database may have practical significance for prognostic risk assessment of AKI and later intervention.

Relationship between platelet/lymphocyte ratio and prognosis of patients with septic acute kidney injury: A pilot study.

BACKGROUND: To explore the potential role of the platelet/lymphocyte ratio (PLR) as a prognostic marker in septic patients with acute kidney injury (AKI) and to provide theoretical evidence for the epidemiological study of the prognosis of patients with septic AKI in its early stage. METHODS: A pilot study was conducted. A logistic regression analysis was conducted to screen the risk factors, and the selected factors were performed using multiple logistic regression analysis; a Receiver Operating Characteristic curve was used to determine the optimal cutoff value of the PLR and then to calculate the sensitivity and specificity of the PLR ratio. RESULTS: Mechanical ventilation, platelet count, PLR, and arterial blood lactate concentration have a correlation with sepsis (p < 0.05). An elevated PLR is significantly associated with a worse prognosis of sepsis-induced AKI (higher mortality). CONCLUSION: The PLR might be an effective factor in predicting a worse prognosis of septic AKI patients.

Use of a smartphone for imaging, modelling, and evaluation of keloids.

OBJECTIVE: We used a smartphone to construct three-dimensional (3D) models of keloids, then quantitatively simulate and evaluate these tissues. METHODS: We uploaded smartphone photographs of 33 keloids on the chest, shoulder, neck, limbs, or abdomen of 28 patients. We used the parallel computing power of a graphics processing unit to calculate the spatial co-ordinates of each pixel in the cloud, then generated 3D models. We obtained the longest diameter, thickness, and volume of each keloid, then compared these data to findings obtained by traditional methods. RESULTS: Measurement repeatability was excellent: intraclass correlation coefficients were 0.998 for longest diameter, 0.978 for thickness, and 0.993 for volume. When measuring the longest diameter and volume, the results agreed with Vernier caliper measurements and with measurements obtained after the injection of water into the cavity. When measuring thickness, the findings were similar to those obtained by ultrasound. Bland-Altman analyses showed that the ratios of 95% confidence interval extremes were 3.03% for longest diameter, 3.03% for volume, and 6.06% for thickness. CONCLUSION: Smartphones were used to acquire data that was then employed to construct 3D models of keloids; these models yielded quantitative data with excellent reliability and validity. The smartphone can serve as an additional tool for keloid diagnosis and research, and will facilitate medical treatment over the internet.

Development of a risk stratification-based model for prediction of acute kidney injury in critically ill patients.

Acute kidney injury (AKI) is a complex syndrome with a variety of possible etiologies and symptoms. It is characterized by high mortality and poor recovery of renal function. The incidence and mortality rates of patients with AKI in intensive care units are extremely high. It is generally accepted that early identification and prompt treatment of AKI are essential to improve outcomes. This study aimed to develop a model based on risk stratification to identify and diagnose early stage AKI for improved prognosis in critically ill patients.This was a single-center, retrospective, observational study. Based on relevant literature, we selected 13 risk factors (age, sex, hypertension, diabetes, coronary heart disease, chronic kidney disease, total bilirubin, emergency surgery, mechanical ventilation, sepsis, heart failure, cancer, and hypoalbuminemia) for AKI assessment using the Kidney Disease Improving Global Outcomes (KDIGO) diagnostic criteria. Univariate and multivariate analyses were used to determine risk factors for eventual entry into the predictive model. The AKI predictive model was established using binary logistic regression, and the area under the receiver operating characteristic curve (AUROC or AUC) was used to evaluate the predictive ability of the model and to determine critical values.The AKI predictive model was established using binary logistic regression. The AUROC of the predictive model was 0.81, with a sensitivity of 69.8%, specificity of 83.4%, and positive likelihood ratio of 4.2.A predictive model for AKI in critically ill patients was established using 5 related risk factors: heart failure, chronic kidney disease, emergency surgery, sepsis, and total bilirubin; however, the predictive ability requires validation.

Development and validation of risk-stratification delirium prediction model for critically ill patients: A prospective, observational, single-center study.

The objective is to develop a model based on risk stratification to predict delirium among adult critically ill patients and whether early intervention could be provided for high-risk patients, which could reduce the incidence of delirium.We designed a prospective, observational, single-center study. We examined 11 factors, including age, APACHE-II score, coma, emergency operation, mechanical ventilation (MV), multiple trauma, metabolic acidosis, history of hypertension, delirium and dementia, and application of Dexmedetomidine Hydrochloride. Confusion assessment method for the intensive care unit (CAM-ICU) was performed to screen patients during their ICU stay. Multivariate logistic regression analysis was used to develop the model, and we assessed the predictive ability of the model by using the area under the receiver operating characteristics curve (AUROC).From May 17, 2016 to September 25, 2016, 681 consecutive patients were screened, 61 of whom were excluded. The most frequent reason for exclusion was sustained coma 30 (4.4%), followed by a length of stay in the ICU < 24 hours 18 (2.6%) and delirium before ICU admission 13 (1.9%). Among the remaining 620 patients (including 162 nervous system disease patients), 160 patients (25.8%) developed delirium, and 64 (39.5%) had nervous system disease. The mean age was 55 ±â€Š18 years old, the mean APACHE-II score was 16 ±â€Š4, and 49.2% of them were male. Spearman analysis of nervous system disease and incidence of delirium showed that the correlation coefficient was 0.186 (P < .01). We constructed a prediction model that included 11 risk factors. The AUROC was 0.78 (95% CI 0.72-0.83).We developed the model using 11 related factors to predict delirium in critically ill patients and further determined that prophylaxis with Dexmedetomidine Hydrochloride in delirious ICU patients was beneficial. Patients who suffer from nervous system disease are at a higher incidence of delirium, and corresponding measures should be used for prevention. TRIAL REGISTRATION: ChiCTR-OOC-16008535.