Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.

Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes. The results showed that the expression levels of four genes and six ratios of expression changed time-dependent during wound repair. The six expression ratios provided additional temporal information, distinct from the four genes analyzed separately by principal component analysis. The overall performance metrics for cross-validation and external validation of four typical prediction models were improved when six ratios of expression were added as additional input variables. Overall, expression ratios among genes provide temporal information and have excellent potential as predictive markers for wound age estimation. Combining the expression levels of genes with ratio-expression of genes may allow for more accurate estimates of the time of injury.

Derivation and elimination of uremic toxins from kidney-gut axis.

Uremic toxins are chemicals, organic or inorganic, that accumulate in the body fluids of individuals with acute or chronic kidney disease and impaired renal function. More than 130 uremic solutions are included in the most comprehensive reviews to date by the European Uremic Toxins Work Group, and novel investigations are ongoing to increase this number. Although approaches to remove uremic toxins have emerged, recalcitrant toxins that injure the human body remain a difficult problem. Herein, we review the derivation and elimination of uremic toxins, outline kidney-gut axis function and relative toxin removal methods, and elucidate promising approaches to effectively remove toxins.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans.

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

OBJECTIVES: To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods. METHODS: Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis. RESULTS: The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples. CONCLUSIONS: The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Effect of Iron-Erythrocyte Metabolism-Related Indexes on Posttraumatic Growth in Patients on Maintenance Hemodialysis (MHD).

Purpose: To investigate the effect of iron-erythrocyte metabolism-related indexes on posttraumatic growth in MHD patients and their caregivers. Patients and Methods: A total of 170 pairs of MHD patients and their caregivers in Shanghai Changhai Hospital were enrolled in this research, which used sociodemographic characteristics, the Posttraumatic Growth Inventory (PTGI), the Perceived Social Support Scale (PSSS), and the Medical Coping Modes Questionnaire (MCMQ). The test data of 141 patients were retrieved from the hospital database. Results: Single-factor analysis showed that the PTGI score of patients with a mean corpuscular erythrocyte volume ≥ 100 fL was 85.4 ± 19.8 and those with a mean corpuscular erythrocyte volume lower than 100 fL were 70.6 ± 24.7; the PTGI scores of patients with reticulocytes >1.5% were 68.8 ± 25.8, and those with reticulocytes <1.5% were 78.4 ± 21.1; the PTGI scores of the caregivers whose serum iron was >10.6 µmol /L were 78.2 ± 21.6, and those with serum iron <10.6 µmol /L were 67.9 ± 22.8. The difference in MCMQ scores between the caregivers with transferrin saturation>50% and with transferrin saturation<20% was 18.9 ± 8.4. For the correlation test of serum iron, reticulocyte and PTGI scores for patients, the Pearson correlation coefficients were 0.239 and -0.193, respectively, and the correlation test between erythrocyte distribution width SD and the score of caregivers MCMQ scale, the Pearson correlation coefficient was 0.225; p for all was< 0.05, with significant differences. There was no significant difference in the scores of different scales for total iron binding capacity (TIBC) at different levels. Conclusion: The indexes related to iron erythrocyte metabolism in MHD patients are correlated with ruminant meditation of patients and their caregivers and promotion of posttraumatic growth. Good nutritional status, adequate hematopoietic material, and normal erythrocyte count and function are also important for them.

[Analysis of correlation between breast cancer molecular subtypes and prognosis of patients receiving breast-conserving therapy].

OBJECTIVE: To evaluate the effects of molecular subtypes on the prognosis of breast cancer patients after BCT (breast-conserving therapy). METHODS: From January 1999 to December 2007, a total of 699 female patients on BCT were analyzed retrospectively. They were grouped according to the results of immunohistochemistry, they were grouped as Luminal A (ER+/PR+/HER2-, Ki-67 < 14%), Luminal B (ER+/PR+/HER2 ± , Ki-67 > 14%), HER2 (ER-/PR-/HER2+) and triple negative breast cancer (TNBC) (ER-/PR-/HER2-). RESULTS: Among them, the recurrence rate, metastasis rate and survival rate within 5 years after BCT accounted for 6.0%, 6.0% and 91.8% respectively. There were statistical differences in metastasis rate and survival rate among patients with different molecular subtypes (P < 0.001), lymph node status (P < 0.001) and on chemotherapy or not (P < 0.001) on multivariate analysis. Though, on univariate analysis, different molecular subtypes had different risks for local failure (P = 0.022). On multivariate analysis, this difference was not statistically significant (P = 0.081). CONCLUSION: Despite a worse prognosis of TNBC versus other subtypes, BCT is a viable option for selected TNBC patients.