Previous studies have demonstrated that patients with traumatic brain injury (TBI) who also have progressive hemorrhagic injury (PHI), have a higher risk of clinical deterioration and worse outcomes than do TBI patients without PHI. Therefore, the early prediction of PHI occurrence is useful to evaluate the status of patients with TBI and to improve outcomes. The objective of this study was to develop and validate a prognostic model that uses information available at admission to determine the likelihood of PHI after TBI. Retrospectively collected data were used to develop a PHI prognostic model with a logistic regression analysis. The prediction model was validated in 114 patients from a separate hospital. Eight independent prognostic factors were identified: age ‡ 57 years (5 points), intra-axial bleeding/brain contusion (4 points), midline shift ‡ 5 mm (6 points), platelet (PLT) count < 100 · 10 9 /L (10 points), PLT count ‡ 100 but < 150 · 10 9 /L (4 points), prothrombin time > 14 sec (7 points), D-dimer ‡ 5 mg/L (12 points), and glucose ‡ 10 mmol/L (10 points). Each patient was assigned a number of points proportional to the regression coefficient. We calculated risk scores for each patient and defined three risk groups: low risk (0-13 points), intermediate risk (14-22 points), and high risk (23-54 points). In the development cohort, the PHI rates after TBI for these three groups were 10.3%, 47.3%, and 85.2%, respectively. In the validation cohort, the corresponding PHI rates were 10.9%, 47.3%, and 86.9%. The C-statistic for the point system was 0.864 ( p = 0.509 by the Hosmer-Lemeshow test) in the development cohort, and 0.862 ( p = 0.589 by the Hosmer-Lemeshow test) in the validation cohort. In conclusion, a relatively simple risk score using admission predictors accurately predicted the risk for PHI after TBI.

This study sought to describe and evaluate any relationship between D-dimer values and progressive hemorrhagic injury (PHI) after traumatic brain injury (TBI). In patients with TBI, plasma D-dimer was measured while a computed tomography (CT) scan was conducted as soon as the patient was admitted to the emergency department. A series of other clinical and laboratory parameters were also measured and recorded. A logistic multiple regression analysis was used to identify risk factors for PHI. A cohort of 194 patients with TBI was evaluated in this clinical study. Eighty-one (41.8%) patients suffered PHI as determined by a second CT scan. The plasma D-dimer level was higher in patients who demonstrated PHI compared with those who did not (P < 0.001. Using a receiver-operator characteristic curve to predict the possibility by measuring the D-dimer level, a value of 5.00 mg/L was considered the cutoff point, with a sensitivity of 72.8% and a specificity of 78.8%. Eight-four patients had D-dimer levels higher than the cut point value (5.0 mg/L); PHI was seen in 71.4% of these patients and in 19.1% of the other patients (P < 0.01). Factors with P < 0.2 on bivariate analysis were included in a stepwise logistic regression analysis to identify independent risk factors for TBI coagulopathy. Logistic regression analysis showed that the D-dimer value was a predictor of PHI, and the odds ratio (OR) was 1.341 with per milligram per liter (P = 0.020). The stepwise logistic regression also identified that time from injury to the first CT shorter than 2 h (OR = 2.118, P = 0.047), PLT counts lesser than 100 x 109/L (OR = 7.853, P = 0.018), and Fg lower than 2.0 g/L (OR = 3.001, P = 0.012) were risk factors for the development of PHI. When D-dimer values were dichotomized at 5 mg/L, time from injury to the first CT scan was no longer a risk factor statistically while the OR value of D-dimer to the occurrence of PHI elevated to 11.850(P < 0.001). The level of plasma D-dimer after TBI can be a useful prognostic factor for PHI and should be considered in the clinical management of patients in combination with neuroimaging and other data.

Background. A substantial increase in histone deacetylase 3 (HDAC3) expression is implicated in the pathological process of diabetes and stroke. However, it is unclear whether HDAC3 plays an important role in diabetes complicated with stroke. We aimed to explore the role and the potential mechanisms of HDAC3 in cerebral ischemia/reperfusion (I/R) injury in diabetic state. Methods. Diabetic mice were subjected to 1 h ischemia, followed by 24 h reperfusion. PC12 cells were exposed to high glucose for 24 h, followed by 3 h of hypoxia and 6 h of reoxygenation (H/R). Diabetic mice received RGFP966 (the specific HDAC3 inhibitor) or vehicle 30 minutes before the middle cerebral artery occlusion (MCAO), and high glucose-incubated PC12 cells were pretreated with RGFP966 or vehicle 6 h before H/R. Results. HDAC3 inhibition reduced the cerebral infarct volume, ameliorated pathological changes, improved the cell viability and cytotoxicity, alleviated apoptosis, attenuated oxidative stress, and enhanced autophagy in cerebral I/R injury model in diabetic state in vivo and in vitro. Furthermore, we found that the expression of HDAC3 was remarkably amplified, and the Bmal1 expression was notably decreased in diabetic mice with cerebral I/R, whereas this phenomenon was obviously reversed by RGFP966 pretreatment. Conclusions. These results suggested that the HDAC3 was involved in the pathological process of the complex disease of diabetic stroke. Suppression of HDAC3 exerted protective effects against cerebral I/R injury in diabetic state in vivo and in vitro via the modulation of oxidative stress, apoptosis, and autophagy, which might be mediated by the upregulation of Bmal1.

BackgroundGossypium barbadense (Sea Island, Egyptian or Pima cotton) cotton has high fiber quality, however, few studies have investigated the genetic basis of its traits using molecular markers. Genome complexity reduction approaches such as genotyping-by-sequencing have been utilized to develop abundant markers for the construction of high-density genetic maps to locate quantitative trait loci (QTLs).ResultsThe Chinese G. barbadense cultivar 5917 and American Pima S-7 were used to develop a recombinant inbred line (RIL) population with 143 lines. The 143 RILs together with their parents were tested in three replicated field tests for lint yield traits (boll weight and lint percentage) and fiber quality traits (fiber length, fiber elongation, fiber strength, fiber uniformity and micronaire) and then genotyped using GBS to develop single-nucleotide polymorphism (SNP) markers. A high-density genetic map with 26 linkage groups (LGs) was constructed using 3557 GBS SNPs spanning a total genetic distance of 3076.23 cM at an average density of 1.09 cM between adjacent markers. A total of 42 QTLs were identified, including 24 QTLs on 12 LGs for fiber quality and 18 QTLs on 7 LGs for lint yield traits, with LG1 (9 QTLs), LG10 (7 QTLs) and LG14 (6 QTLs) carrying more QTLs. Common QTLs for the same traits and overlapping QTLs for different traits were detected. Each individual QTLs explained 0.97 to 20.7% of the phenotypic variation.ConclusionsThis study represents one of the first genetic mapping studies on the fiber quality and lint yield traits in a RIL population of G. barbadense using GBS-SNPs. The results provide important information for the subsequent fine mapping of QTLs and the prediction of candidate genes towards map-based cloning and marker-assisted selection in cotton.

Drought is one of the main abiotic stresses that seriously influences cotton production. Many indicators can be used to evaluate cotton drought tolerance, but the key indicators remain to be determined. The objective of this study was to identify effective cotton drought tolerance indicators from 19 indices, including morphology, photosynthesis, physiology, and yield-related indices, and to evaluate the yield potential of 104 cotton varieties under both normal and drought-stress field conditions. Combined with principal component analysis (PCA) and a regression analysis method, the results showed that the top five PCs among the 19, with eigenvalues &gt; 1, contributed 65.52, 63.59, and 65.90% of the total variability during 2016 to 2018, respectively, which included plant height (PH), effective fruit branch number (EFBN), single boll weight (SBW), transpiration rate (Tr) and chlorophyll (Chl). Therefore, the indicator dimension decreased from 19 to 5. A comparison of the 19 indicators with the 5 identified indicators through PCA and a combined regression analysis found that the results of the final cluster of drought tolerance on 104 cotton varieties were basically consistent. The results indicated that these five traits could be used in combination to screen cotton varieties or lines for drought tolerance in cotton breeding programs, and Zhong R2016 and Xin lu zao 45 exhibited high drought tolerance and can be selected as superior parents for good yield performance under drought stress.