Characterization and optimization of carbohydrate production from an indigenous microalga Chlorella vulgaris FSP-E.

In this study, three indigenous microalgae isolates were examined for their ability to produce carbohydrates. Among them, Chlorella vulgaris FSP-E displayed relatively high cell growth rate and carbohydrate content. The carbohydrate productivity of C. vulgaris FSP-E was further improved by using engineering strategies. The results show that using an appropriate light intensity and inoculum size could effectively promote cell growth and carbohydrate productivity. Nitrogen starvation triggered the accumulation of carbohydrates in the microalga, achieving a carbohydrate content of 51.3% after 4-day starvation. Under the optimal conditions, the highest biomass and carbohydrate productivity were 1.437 and 0.631 g L(-1) d(-1), respectively. This performance is better than that reported in most related studies. Since glucose accounted for nearly 93% of the carbohydrates accumulated in C. vulgaris FSP-E, the microalga is an excellent feedstock for bioethanol fermentation.

Bioethanol production using carbohydrate-rich microalgae biomass as feedstock.

This study aimed to evaluate the potential of using a carbohydrate-rich microalga Chlorella vulgaris FSP-E as feedstock for bioethanol production via various hydrolysis strategies and fermentation processes. Enzymatic hydrolysis of C. vulgaris FSP-E biomass (containing 51% carbohydrate per dry weight) gave a glucose yield of 90.4% (or 0.461 g (g biomass)(-1)). The SHF and SSF processes converted the enzymatic microalgae hydrolysate into ethanol with a 79.9% and 92.3% theoretical yield, respectively. Dilute acidic hydrolysis with 1% sulfuric acid was also very effective in saccharifying C. vulgaris FSP-E biomass, achieving a glucose yield of nearly 93.6% from the microalgal carbohydrates at a starting biomass concentration of 50 g L(-1). Using the acidic hydrolysate of C. vulgaris FSP-E biomass as feedstock, the SHF process produced ethanol at a concentration of 11.7 g L(-1) and an 87.6% theoretical yield. These findings indicate the feasibility of using carbohydrate-producing microalgae as feedstock for fermentative bioethanol production.

Analysis of using the tongue deviation angle as a warning sign of a stroke.

BACKGROUND: The symptom of tongue deviation is observed in a stroke or transient ischemic attack. Nevertheless, there is much room for the interpretation of the tongue deviation test. The crucial factor is the lack of an effective quantification method of tongue deviation. If we can quantify the features of the tongue deviation and scientifically verify the relationship between the deviation angle and a stroke, the information provided by the tongue will be helpful in recognizing a warning of a stroke. METHODS: In this study, a quantification method of the tongue deviation angle was proposed for the first time to characterize stroke patients. We captured the tongue images of stroke patients (15 males and 10 females, ranging between 55 and 82 years of age); transient ischemic attack (TIA) patients (16 males and 9 females, ranging between 53 and 79 years of age); and normal subjects (14 males and 11 females, ranging between 52 and 80 years of age) to analyze whether the method is effective. In addition, we used the receiver operating characteristic curve (ROC) for the sensitivity analysis, and determined the threshold value of the tongue deviation angle for the warning sign of a stroke. RESULTS: The means and standard deviations of the tongue deviation angles of the stroke, TIA, and normal groups were: 6.9 ± 3.1, 4.9 ± 2.1 and 1.4 ± 0.8 degrees, respectively. Analyzed by the unpaired Student's t-test, the p-value between the stroke group and the TIA group was 0.015 (>0.01), indicating no significant difference in the tongue deviation angle. The p-values between the stroke group and the normal group, as well as between the TIA group and the normal group were both less than 0.01. These results show the significant differences in the tongue deviation angle between the patient groups (stroke and TIA patients) and the normal group. These results also imply that the tongue deviation angle can effectively identify the patient group (stroke and TIA patients) and the normal group. With respect to the visual examination, 40% and 32% of stroke patients, 24% and 16% of TIA patients, and 4% and 0% of normal subjects were found to have tongue deviations when physicians "A" and "B" examined them. The variation showed the essentiality of the quantification method in a clinical setting. In the receiver operating characteristic curve (ROC), the Area Under Curve (AUC, = 0.96) indicates good discrimination. The tongue deviation angle more than the optimum threshold value (= 3.2°) predicts a risk of stroke. CONCLUSIONS: In summary, we developed an effective quantification method to characterize the tongue deviation angle, and we confirmed the feasibility of recognizing the tongue deviation angle as an early warning sign of an impending stroke.

Using the spring constant method to analyze arterial elasticity in type 2 diabetic patients.

BACKGROUND: This study tests the validity of a newly-proposed spring constant method to analyze arterial elasticity in type 2 diabetic patients. METHODS: The experimental group comprised 66 participants (36 men and 30 women) ranging between 46 and 86 years of age, all with diabetes mellitus. In the experimental group, 21 participants suffered from atherosclerosis. All were subjected to the measurements of both the carotid-femoral pulse wave velocity (cfPWV) and the spring constant method. The comparison (control) group comprised 66 normal participants (37 men and 29 women) with an age range of 40 to 80 years who did not have diabetes mellitus. All control group members were subjected to measurement by the spring constant method. RESULTS: Statistical analysis of the experimental and control groups indicated a significant negative correlation between the spring constant and the cfPWV (P < .001; r = - 0.824 and - 0.71). Multivariate analysis similarly indicated a close relationship. The Student's t test was used to examine the difference in the spring constant parameter between the experimental and control groups. A P-value less than .05 confirmed that the difference between the 2 groups was statistically significant. In receiver operating characteristic curve (ROC), the Area Under Curve (AUC, = 0.85) indicates good discrimination. These findings imply that the spring constant method can effectively identify normal versus abnormal characteristics of elasticity in normal and diabetic participants. CONCLUSIONS: This study verifies the use of the spring constant method to assess arterial elasticity, and found it to be efficient and simple to use. The spring constant method should prove useful not only for improving clinical diagnoses, but also for screening diabetic patients who display early evidence of vascular disease.