Effect of hyperbaric oxygen on post-stroke depression

BACKGROUND

In patients with post-stroke depression (PSD) in diabetes, the situation may be more complex, requiring simultaneous treatment of blood glucose, depressive symptoms, and neurological dysfunction. Hyperbaric oxygen (HBO) therapy can improve tissue oxygen content and improve the situation of ischemia and hypoxia, thus playing a role in protecting brain cells and restoring the function of brain cells. However, there are few studies on HBO therapy for patients with PSD. This study explores the clinical efficacy of such therapy for stroke complicated with depression and diabetes mellitus, and to provide reference and basis for clinical treatment and development through the application of relevant rating scales and laboratory test indicators.

AIM

To evaluate the clinical effects of HBO therapy on patients with diabetes with PSD.

METHODS

A total of 190 diabetic patients with PSD were randomly divided into observation and control groups (95 patients per group). The control group received escitalopram oxalate 10mg once a day for eight weeks. In addition, the ob-servation group was also given HBO therapy, once a day, five times a week, for eight weeks. The Montgomery Depression Rating Scale (MADRS), National Institutes of Health Stroke Scale (NIHSS), hypersensitive C-reactive protein, tumor necrosis factor (TNF)-α, and fasting glucose levels were compared.

RESULTS

There were no significant differences in age, sex, or depression course between the groups (P > 0.05). After HBO treatment, MADRS scores in both groups decreased significantly (14.3 ± 5.2), and were significantly lower in the control group (18.1 ± 3.5). After HBO treatment, NIHSS scores in both groups decreased significantly, and scores in the observation group (12.2 ± 4.0) decreased more than in the control group (16.1 ± 3.4), the difference was statistically significant (P < 0.001). The levels of hypersensitive C-reactive protein and TNF-α in both groups were significantly decreased, and the observation group was significantly lower than the control group (P < 0.001). Fasting blood glucose levels in both groups decreased significantly, and those in the observation group decreased more (8.02 ± 1.10) than in the control group (9.26 ± 1.04), with statistical significance (t = -7.994, P < 0.001).

CONCLUSION

HBO therapy can significantly improve depressive symptoms and neurological dysfunction in patients with PSD, and reduce the levels of hypersensitive C-reactive protein, TNF-α and fasting blood glucose.

Effects of spatial variation in water quality and hydrological factors on environmental flows

Environmental flow is the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend on these ecosystems. Environmental flows (e-flows) are crucial parameters for ecosystem restoration. Understanding the effects of spatial variation in the hydrological and water quality factors on e-flows aids the determination of recovery prior areas and helps to improve the success rate of ecosystem restoration projects. However, few studies have investigated the effects, which severely hinder the restoration of aquatic ecosystems and the sustainable use of water resources in inland waters. This paper therefore presents a framework for studying such effects. Spatial autocorrelation, a geostatistical method, is used to analyze the spatial variation in the hydrological and water quality factors and to further analyze the effects of various factors on the spatial heterogeneity of e-flows. Four different methods including the Tennant method, wetted perimeter method, AEHRA, and integrated water quality method are integrated to comprehensively evaluate e-flows. The former three methods consider the demands of biota on the streamflow, whereas the latter considers the demands on both the streamflow and the water quality. The results show that the Tennant and wetted perimeter methods, which focus on the statistics of only streamflow, result in similar spatial distribution of e-flows; the AEHRA and integrated water quality method, which consider the effects of water quality and other hydrological factors such as flow velocity and water depth on fish, also result in a similar spatial variation. Consideration of both demands on the hydrological factors and the water quality environmental factors makes the integrated water quality method more practical, particularly in developing regions with excessive pollutant discharge into rivers. In addition, spatial variation in the hydrological and water quality factors influenced the presence of principal fish species and consequently affected the e-flows. Of the 37 water quality factors identified, water transparency had a negative impact on e-flow because the increase in transparency could reduce the number of principal fish species. Of the four hydrological factors, flow velocity and river width had positive impacts on fish because the increase in flow velocity can provide breeding sites and habitats for more fish, respectively, both of which result in increases in the numbers of principal fish species. We found that spatial variation in the hydrology and water quality factors had a profound impact on the living environments of aquatic organisms; negative changes in these factors lowered the survival probability of principal species, which changed the hierarchy and structure of the ecosystems and thus led to variation in e-flows. The results can provide priori knowledge for e-flow methods selection and a reference for ecosystem restoration helping improve the success rate of project elsewhere in the world.

Predicting cyanobacteria bloom occurrence in lakes and reservoirs before blooms occur

With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014-2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5-32.5 °C, 7.0-9.38, 0.13-0.22 mg L^-1, 0.38-0.63 mg L^-1, 10.5-17.5 mg L^-1, and 4.97-8.28 mg L^-1, respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.

Quantitative assessment of the effects of human activities on phytoplankton communities in lakes and reservoirs

Global algal blooms have been severely threatening safety of drinking water and development of socio-economy. Effective prevention and accurate control of algal blooms require a quantitative assessment of the influence of human activities and identification of priority areas. However, previous studies on the quantitative assessment of the effects of human activities on algal communities are lacking, severely hindering the effective and precise control of algal blooms. This paper proposes a quantitative assessment model to evaluate the impact intensity of human activities on phytoplankton. Applications showed that the proliferation of phytoplankton were more limited by nutrients such as total phosphorus and ammonia where waters are less influenced by human activities, yet were less limited by these nutrients where there are highly intensive human activities. The density of phytoplankton in waters increased with an increase in human activity intensity, particularly in concentrated agricultural areas, which are priority areas for the prevention and control of algal blooms. The methodologies can clearly identify key areas for algal bloom prevention and control and can provide scientific evidence for water and nutrient management throughout the world, reducing the risk of algal blooms and ensuring aquatic ecosystem health and potable water safety.

Diurnal, photoperiodic, and age-related changes in plasma growth hormone levels in the golden hamster

The golden hamster has been used extensively as an animal model for the study of both circadian and seasonal rhythms, and their regulation by the light-dark (LD) cycle. More recently, this species has been used to examine how the generation and entrainment of circadian rhythms are altered in advanced age. Recent studies in both humans and rodents indicate that age-related changes in the diurnal rhythm of pituitary growth hormone (GH) release may mediate some of the adverse effects of aging on a variety of physiological systems. As a first step in determining whether or not age-related changes in circulating GH levels are associated with changes in the regulation and/or expression of circadian rhythms, the effects of age on both the ultradian and diurnal patterns of plasma GH levels were determined in 3- to 22-month-old male hamsters that were bled every 15 min for a 24-hr period while entrained to an LD 14:10 light cycle. An additional study involving a similar blood collection protocol examined whether or not the length of the day is involved in the regulation of plasma GH levels. Although the frequency of pulsatile GH release did not change with advanced age, both the mean levels of GH per sample and the mean amplitude per pulse of GH were significantly elevated in 3- to 4-month-old animals, compared to animals that were 12-13, 15-16, or 21-22 months of age. In hamsters aged 3-4 and 12-13 months, there was an increase in both mean levels and the mean amplitude per pulse of GH, but not pulse frequency, during the night as compared to daytime values. No such diurnal rhythm was detected in the two groups of older animals. A clear diurnal rhythm in GH levels was also detected in animals maintained in a short-day (LD 6:18) cycle, and the mean levels of GH per sample were greater in hamsters maintained on short compared to long (LD 14:10) days. These results indicate that there are pronounced age-related changes in pituitary GH release in the hamster, and that both the time of day and the length of the day influence the pattern of GH release.