Safinamide alleviates hyperalgesia via inhibiting hyperexcitability of DRG neurons in a mouse model of Parkinson's disease.

Pain is a widespread non-motor symptom that presents significant treatment challenges in patients with Parkinson's disease (PD). Safinamide, a new drug recently introduced for PD treatment, has demonstrated analgesic effects on pain in PD patients, though the underlying mechanisms remain unclear. To investigate the analgesic and anti-PD effect of safinamide, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model was used, and rasagiline as positive control on motor symptoms. Notably, only safinamide alleviated hyperalgesia in MPTP mice. Whole-cell patch-clamp recordings of dorsal root ganglion (DRG) neurons revealed hyperexcitability in MPTP mice, which safinamide counteracted in a concentration-dependent manner. The voltage clamp further demonstrated that sodium current in DRG neurons of MPTP mice was enhanced and safinamide reduced sodium current density. RT-qPCR identified upregulated Nav1.7 and Nav1.8 transcripts (Scn9a and Scn10a) in DRG neurons of MPTP mice. Our results suggest that safinamide could relieve hyperalgesia by inhibiting DRG neuron hyperexcitability in MPTP mice.

PACAP/PAC1-R activation contributes to hyperalgesia in 6-OHDA-induced Parkinson's disease model rats via promoting excitatory synaptic transmission of spinal dorsal horn neurons.

Pain is a common annoying non-motor symptom in Parkinson's disease (PD) that causes distress to patients. Treatment for PD pain remains a big challenge, as its underlying mechanisms are elusive. Pituitary adenylate cyclase-activating polypeptide (PACAP) and its receptor PAC1-R play important roles in regulating a variety of pathophysiological processes. In this study, we investigated whether PACAP/PAC1-R signaling was involved in the mechanisms of PD pain. 6-hydroxydopamine (6-OHDA)-induced PD model was established in rats. Behavioral tests, electrophysiological and Western blotting analysis were conducted 3 weeks later. We found that 6-OHDA rats had significantly lower mechanical paw withdrawal 50% threshold in von Frey filament test and shorter tail flick latency, while mRNA levels of Pacap and Adcyap1r1 (gene encoding PAC1-R) in the spinal dorsal horn were significantly upregulated. Whole-cell recordings from coronal spinal cord slices at L4-L6 revealed that the frequency of spontaneous excitatory postsynaptic currents (sEPSCs) in dorsal horn neurons was significantly increased, which was reversed by application of a PAC1-R antagonist PACAP 6-38 (250 nM). Furthermore, we demonstrated that intrathecal microinjection of PACAP 6-38 (0.125, 0.5, 2 µg) dose-dependently ameliorated the mechanical and thermal hyperalgesia in 6-OHDA rats. Inhibition of PACAP/PAC1-R signaling significantly suppressed the activation of Ca2+/calmodulin-dependent protein kinase II and extracellular signal-regulated kinase (ERK) in spinal dorsal horn of 6-OHDA rats. Microinjection of pAAV-Adcyap1r1 into L4-L6 spinal dorsal horn alleviated hyperalgesia in 6-OHDA rats. Intrathecal microinjection of ERK antagonist PD98059 (10 µg) significantly alleviated hyperalgesia in 6-OHDA rats associated with the inhibition of sEPSCs in dorsal horn neurons. In addition, we found that serum PACAP-38 concentration was significantly increased in PD patients with pain, and positively correlated with numerical rating scale score. In conclusion, activation of PACAP/PAC1-R induces the development of PD pain and targeting PACAP/PAC1-R is an alternative strategy for treating PD pain.

Inorganic quantum dots - anammox consortia hybrid for stable nitrogen elimination under high-intensity solar-simulated irradiation.

External stimulus such as light irradiation is able to deteriorate intracellular redox homeostasis and induce photooxidative damage to non-photogenic bacteria. Exploiting effective strategies to help bacteria resisting infaust stress is meaningful for achieving a stable operation of biological treatment system. In this work, selenium-doped carbon quantum dots (Se-CQDs) were blended into anaerobic ammonia oxidation (anammox) bacteria and an inorganic nanoparticle-microbe hybrid was successfully fabricated to evaluate its nitrogen removal performance under solar-simulated irradiation. It was found that the specific anammox activity decreased by 29.7 ± 5.2% and reactive oxygen species (ROS) content increased by 134.8 ± 4.1% under 50,000 lux light. Sludge activity could be completely recovered under the optimum dosage of 0.42 mL·(g volatile suspended solid) -1 Se-CQDs. Hydroxyl radical (·OH) and superoxide anion radical (·O2-) were identified as the leading ROS inducing lipid peroxidation and antioxidase function detriment. Also, the structure of ladderane lipids located on anammoxosome was destroyed by ROS and functional genes abundances declined accordingly. Although cell surface coated Se-CQDs could absorb ultraviolet light and partially mitigated the photoinhibition, the direct scavenging of ROS by intracellular Se-CQDs primarily contributed to the cellular redox homeostasis, antioxidase activity recovery and sludge activity improvement. The findings of this work provide in-depth understanding the metabolic response mechanism of anammox consortia to light irradiation and might be valuable for a more stable and sustainable nitrogen removal technology, i.e., algal-bacterial symbiotic system, development.

A review of anammox-based nitrogen removal technology: From microbial diversity to engineering applications.

The anaerobic ammonium oxidation (anammox) process has the advantages of high efficiency and low energy consumption, so it has broad application prospects in biological denitrification of wastewater. However, the application of anammox technology to existing wastewater treatment is still challenging. The main problems are the insufficient supply of nitrite and the susceptibility of anammox bacteria to environmental factors. In this paper, from the perspective of the diversity of anammox bacteria, the habitats and characteristics of anammox bacteria of different genera were compared. At the same time, laboratory research and engineering applications of anammox technology in treating wastewater from different sources were reviewed, and the progress of and obstacles to the practical application of anammox technology were clarified. Finally, a focus for future research was proposed to intensively study the water quality barrier factors of anammox and its regulation strategies. Meanwhile, a combined process was developed and optimized on this basis.

Inhibition of Spinal 5-HT3 Receptor and Spinal Dorsal Horn Neuronal Excitability Alleviates Hyperalgesia in a Rat Model of Parkinson's Disease.

Pain in Parkinson's disease (PD) is increasingly recognized as a major factor associated with poor life quality of PD patients. However, classic therapeutic drugs supplying dopamine have limited therapeutic effects on PD-related pain. This suggests that there is a mechanism outside the dopamine system that causes pain in PD. Our previous study demonstrated that 6-OHDA induced PD model manifested hyperalgesia to thermal and mechanical stimuli and decreased serotonin (5-hydroxytryptamine; 5-HT) in the spinal dorsal horn (SDH). Several 5-HT receptor subtypes have been confirmed to be associated with nociception in the spinal cord, such as 5-HT1A receptor, 5-HT1B receptor, 5-HT2 receptor, 5-HT3 receptor, and 5-HT7 receptor. Most research has shown that 5-HT1A receptor and 5-HT3 receptor play a key role in pain transmission in the spinal cord. We hypothesized that hyperalgesia of 6-OHDA rats may be related to increased excitability of SDH neurons, and functional change of 5-HT3 receptor may reverse the hyperalgesia of 6-OHDA lesioned rats and decrease cell excitability of SDH neurons. To test this hypothesis, we used whole-cell patch-clamp and pharmacological methods to evaluate the effect of 5-HT3 receptor and 5-HT1A receptor on the hyperalgesia of 6-OHDA rats. The results suggested that increased excitability in SDH neurons could be reversed by 5-HT3 receptor antagonist ondansetron (20 µmol/L) and palosetron (10 µmol/L), but not 5-HT3 receptor agonist m-CPBG (30 µmol/L) and SR 57,727 (10 µmol/L), 5-HT1A receptor agonist 8-OH DPAT (10 µmol/L) and eptapirone (10 µmol/L) and 5-HT1A receptor antagonist WAY-100635 (10 µmol/L) and p-MPPI (10 µmol/L). Intrathecal injection of ondansetron (0.1 mg/kg) but not m-CPBG (0.1 mg/kg), 8-OH DPAT (0.1 mg/kg), and WAY-100635 (0.1 mg/kg) significantly attenuated the mechanical hyperalgesia and thermal hyperalgesia in 6-OHDA lesioned rats. In conclusion, the present study suggests that inhibition of spinal 5-HT3 receptor and SDH neuronal excitability alleviates hyperalgesia in PD rats. Our study provides a novel mechanism or therapeutic strategy for pain in patients with PD.

Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.

BACKGROUND: Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. METHODS: A total of 21,139 records of ICU stays were analysed and 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performance of the attention-based TCN with that of traditional artificial intelligence (AI) methods. RESULTS: The area under receiver operating characteristic (AUCROC) and area under precision-recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837 (0.824 -0.850) and 0.454, respectively. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, respectively, compared to the traditional AI method, which had a low sensitivity (< 50%). CONCLUSIONS: The attention-based TCN model achieved better performance in the prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. The attention-based TCN mortality risk model has the potential for helping decision-making for critical patients. TRIAL REGISTRATION: Data used for the prediction of mortality risk were extracted from the freely accessible MIMIC III dataset. The project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified. The data were accessed via a data use agreement between PhysioNet, a National Institutes of Health-supported data repository (https://www.physionet.org/), and one of us (Yu-wen Chen, Certification Number: 28341490). All methods were carried out in accordance with the institutional guidelines and regulations.

A review on characterizing the metabolite property of anammox sludge by spectroscopy.

As one of the most promising autotrophic biological nitrogen removal technology, anaerobic ammonia oxidation (anammox) has gained intense attention for the past decades and several full-scale facilities have been implemented worldwide. However, anammox bacteria are easily affected by disturbed external environmental factors, which commonly leads to the fluctuations in reactor performance. The response of anammox sludge to external stress results in changes in components and structural characteristics of intracellular and extracellular polymer substances. Real-time and convenient spectral analysis of anammox sludge metabolites can give early warning of performance deterioration under external stresses, which is of great significance to the stable operation of bioreactor. This review summarized the research progress on characterizing the intracellular and extracellular metabolites of anammox sludge through spectroscopic techniques. The correlation between anammox sludge activity and its key metabolites was analyzed. Also, the limitations and future prospects of applying spectral analytical techniques for anammox bioreactor monitoring were discussed and outlooked. This review may provide valuable information for both scientific study and engineering application of anammox based nitrogen removal technology.

A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.

BACKGROUND: Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI). METHODS: We collected surgical patients' non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MSE), and the Bland-Altman analysis. RESULTS: For EBL, the R2, MAE and MSE for the method based on DenseNet were 0.966 (95% CI: 0.962-0.971), 0.186 (95% CI: 0.167-0.207) and 0.096 (95% CI: 0.084-0.109), respectively. For EHL, the R2, MAE and MSE for the method based on DenseNet were 0.941 (95% CI: 0.934-0.948), 0.325 (95% CI: 0.293-0.355) and 0.284 (95% CI: 0.251-0.317), respectively. The accuracies of EBL and EHL based on DenseNet were more satisfactory than that of FET. Bland-Altman analysis revealed a bias of 0.02 ml with narrow limits of agreement (LOA) (-0.47 to 0.52 mL) and of 0.05 g with narrow LOA (-0.87 to 0.97 g) between the methods based on DenseNet and actual blood loss and Hb loss. CONCLUSIONS: We developed a simpler and more accurate AI-based method for EBL and EHL, which may be more fit for surgeries primarily using sponges and with a small to medium amount of blood loss.