Protective Effects of Dexmedetomidine on Hippocampal Neurons in Rats Anesthetized with Sevoflurane

To explore effects of dexmedetomidine (Dex) on cognitive function and hippocampal neuronal apoptosis in rats anesthetized with sevoflurane (Sevo), and regulation of brain-derived neurotrophic factor (BDNF) and its downstream signaling. 30 Sprague-Dawley (SD) rats were randomly divided into control group inhaled 29% concentration oxygen), Sevo group (2 L/min oxygen flow +1.5% Sevo), Dex+Sevo group (after injection of 20 μg/kg Dex, treated with 2L/min oxygen flow+1.5% Sevo). Haematoxylin and eosin (HE) staining and Nissl’s staining were adopted to detect morphological and functional changes in hippocampus of rats. Apoptosis was detected by immunofluorescence, BDNF expression was detected by immunohistochemistry. Reverse transcription PCR (RT-PCR) was conducted to detect mRNA expression of key proteins in downstream signaling of BDNF. The results showed that Sevo induced apoptosis of hippocampus neurons, while Dex improved Sevo induced apoptosis. In contrast to the control, the positive expression of BDNF in hippocampus of Sevo group was notably decreased (P < 0.05), and that of Dex+Sevo group was notably higher in contrast to Sevo group (P < 0.05). Signaling pathways of MAPK, PI3K-Akt, and Ras were predicted by String software as the downstream pathways of BDNF. RT-PCR results showed that these 3 signaling pathways were involved in Dex improving Sevo-induced cognitive impairment and hippocampal neuron apoptosis. In conclusion, Dex could improve cognitive dysfunction and hippocampal neuron apoptosis in rats induced by Sevo, and the mechanism was related to upregulation of BDNF expression and activation of pathways of MAPK, PI3K-Akt, and Ras.

Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care

Background Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. Methods We modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021. Results The mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08–3.06) reduction (P < 0.001) from 7.94% under the standard of care to 5.37% under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14–1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic. Conclusions A personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists’ actual flow rate matched the RL decisions.

ANALYSIS OF BIOGAS COMPONENT PRODUCTION DURING ANAEROBIC DIGESTION OF SOUR CABBAGE IN MICROAERATION CONDITIONS UNDER DIFFERENT pH CONDITIONS

In the article, were checked influences of microaeration, pH, and VSS (Volatile Suspended Solid) for sour cab-bage anaerobic digestion. Results fermentation of sour cabbage under the condition of small oxygen addition are presented in this research can be classified as dark fermentation or hydrogenotrophic anaerobic digestion. The investigations were carried out for two concentrations 5 g VSS /L and 10 g VSS /L of sour cabbage at pH 6.0. The oxygen flow rates (OFR) for 5 g VSS /L were in the range of 0.53 to 3.3 mL/h for obtaining 2% to 8% of oxygen. In cases of low pH and microaeration, ethylene production was observed at a level below 0.05% in biogas. The highest volume of hydrogen for 5 g VSS/L was obtained for flow rate 0.58 O2 mL/h, giving hydrogen concentration in biogas in the range of 0 to 20%. For VSS 5 g/L and oxygen flow rate 0.58 mL/h; 0.021 L of hydrogen is produced per gram of VSS. In this case, VSS 10 g/L and oxygen flow rate 1.4 mL/h at pH 6.0, 0.03 L of hydrogen is generated per gram. Microaeration from 0.58 mL/h to 0.87 mL/h was propitious for hydrogen production at 5 g VSS/L of sour cabbage and 1.4 mL/h for 10 g/L. Another relevant factor is the volatile suspended solid factor of sour cabbage that caused optimal hydrogen production at VSS 89.32%.

Optimized Dissolved Oxygen Fuzzy Control for Recombinant Escherichia coli Cultivations

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant Escherichia coli cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and E. coli cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by E. coli during the cultivation.