The postoperative cognitive dysfunction induced by central inflammation with possible involvement of the gut-brain axis

Background: Postoperative cognitive dysfunction is widely recognized as severe postoperative central nervous dysfunction and has a significant impact on the ’patient's physical and mental health. Methods: Postoperative models of tibial fracture in aged rats were established, including the control group, model group, CCL11 protein injection group, and saline injection group. Morris water maze test was used to detect the behavioral characteristics of rats. Enzyme-Linked Immunosorbent Assay was used or determine the content of CCL11 and CXCL10. Immunofluorescence staining was used to detect the distribution of CD14+CD163+macrophages in colon tissues and CD11b+CCR3+microglia cells in hippocampal tissues. Western blot analyzed NOX1 and STAT3 expression in hippocampus tissues. Results: Water maze test results confirmed severe cognitive impairment in CCL11 rats. The content of CCL11 and CXCL10 in the CCL11 group was much higher than that of the model group. The distribution of macrophage and microglia cells in the CCL11 model group was greater than that in the model group and the saline group. The expression of NOX1 and STAT3 in the CCL11 group was higher compared with the model group. Conclusion: Abnormal macrophage function and excessive CCL11 secretion were observed in the rats with lower limb fractures after surgery. Postoperative central inflammation in rats with lower limb fracture induced postoperative cognitive dysfunction through the gut-brain axis molecular mechanism

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis

Antibacterial and antibiofilm activities of protocatechualdehyde and its synergy with ampicillin against methicillin-resistant Staphylococcus aureus

Protocatechualdehyde (PA) is a phenolic acid present in many plants and has many biological activities. Herein, the antagonistic effects and the action mechanism of PA against methicillin-resistant Staphylococcus aureus (MRSA) were studied. The results showed that PA had both significant antibacterial and anti-biofilm activities against MRSA. Additionally, PA had synergy with ampicillin against MRSA. It was elucidated that PA was prominent in destroying cell membranes, increasing cell membrane permeability and intracellular ROS production, thus leading to bacterial cell damage. Transcriptome analysis showed that PA disrupts many physiological pathways, including increasing cell membrane permeability, inhibiting biofilm formation, decreasing resistance to antimicrobial agents, and impairing DNA replication. Finally, the antimicrobial preservation test showed that PA could inhibit the growth of MRSA and prevent the corruption of beef. In summary, PA is an effective natural antibacterial substance and has a good application potential in food preservation, even in tackling antibiotic resistance problems

Ppbv-Level Ethane Detection Using Quartz-Enhanced Photoacoustic Spectroscopy with a Continuous-Wave, Room Temperature Interband Cascade Laser

A ppbv-level quartz-enhanced photoacoustic spectroscopy (QEPAS)-based ethane (C2H6) sensor was demonstrated by using a 3.3 μm continuous-wave (CW), distributed feedback (DFB) interband cascade laser (ICL). The ICL was employed for targeting a strong C2H6 absorption line located at 2996.88 cm−1 in its fundamental absorption band. Wavelength modulation spectroscopy (WMS) combined with the second harmonic (2f) detection technique was utilized to increase the signal-to-noise ratio (SNR) and simplify data acquisition and processing. Gas pressure and laser frequency modulation depth were optimized to be 100 Torr and 0.106 cm−1, respectively, for maximizing the 2f signal amplitude. Performance of the QEPAS sensor was evaluated using specially prepared C2H6 samples. A detection limit of 11 parts per billion in volume (ppbv) was obtained with a 1-s integration time based on an Allan-Werle variance analysis, and the detection precision can be further improved to ~1.5 ppbv by increasing the integration time up to 230 s

Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study.

BACKGROUND With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson's disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome. This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with 18F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD. METHODS Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent 18F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions. RESULTS The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson's Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient. CONCLUSIONS The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients. Clinical Trial Registration ChiCTR-DOC-16008645.  http://www.chictr.org.cn/showproj.aspx?proj=13865

Does the Personality of Patients with Parkinson’s Disease Affect the Decision to Perform Deep Brain Stimulation Surgery? A Cross-Sectional Study in a Chinese Cohort

We investigated whether the personality of patients with Parkinson’s disease (PD) before subthalamic brain stimulation differed from patients receiving drug treatments and whether the personality of patients affected surgical decisions. We recruited 38 patients with advanced PD scheduled for deep brain stimulation (DBS), 40 patients with PD receiving the very best medical treatment, and 51 healthy control subjects. All participants were evaluated by the Minnesota multiphasic personality inventory-1 (MMPI-1). PD patients who were candidates for DBS did not exhibit any significant differences in personality when compared with PD patients who were treated with drugs. Compared with healthy controls, patients with PD had remarkably higher MMPI-1 scores for spiritual quality, neuroticism, and introversion, but significantly lower scores for socialization. In addition, patients with PD were more submissive, more dependent on others, and less active in social activities. Our data indicated that the main deciding factor relating to whether to undergo DBS was the disease itself and not the pathological personality. However, neurotic and psychotic symptoms accompanying PD may influence the effect of DBS. We found that greater benefit is obtained by surgical or medical interventions if abnormal neurotic characteristics are considered early in the course of PD

Performance Enhancement of Methane Detection Using a Novel Self-Adaptive Mid-Infrared Absorption Spectroscopy Technique

An electrical-domain self-adaptive mid-infrared absorption spectroscopy for methane detection based on an interband cascade laser was demonstrated. By adding noise into the laser drive signal, denoising and sensing performances were evaluated for the technique. Experiments were made to study the effects of noise level/type on sensor stability, characterized by Allan deviation. High- and low-frequency noise levels have the same functional variation trend on Allan deviation, which differs from white Gaussian noise. Within a noise level range of 0-125 mV for low- and high-frequency noise and 0-62.5 mV for white Gaussian noise in the mercury-cadmium-telluride detector's output (with a pure signal amplitude of ~300 mV), the sensor stability using self-adaptive denoising was enhanced by a factor of 1.05-20, 1.32-6.25, and 1.15-3.33 times compared to that using no filtering, for the three kinds of noise, respectively. The reported self-adaptive methane sensor system shows enhanced stability compared to the direct laser absorption spectroscopy sensor using traditional sensing architecture and classic filtering method. The sensor was further evaluated through outdoor atmospheric methane measurements using such technique. A second-order self-adaptive direct laser absorption spectroscopy technique was also proposed for noise suppression in both optical and electrical domain as an outlook of the concept of this paper