Manganese-enhanced magnetic resonance assessment of changes in hippocampal neural function after the treatment of radiation-induced brain injury with bone marrow mesenchymal stem cells.

The role of bone marrow mesenchymal stem cells (BMSCs) in treating radiation-induced brain injury (RIBI) is not completely understood, and assessment methods to directly characterize neurological function are lacking. In this study, we aimed to evaluate the effects of BMSCs treatment on changes in hippocampal neural function in Sprague-Dawley(SD) rats with RIBI, and to evaluate the therapeutic effect of BMSCs by manganese-enhanced magnetic resonance imaging (MEMRI). First, we assessed cognitive function after RIBI treatment with BMSCs using the Morris water maze. Next, we used MEMRI at two time points to observe the treatment effect and explore the correlation between MEMRI and cognitive function. Finally, we evaluated the expression of specific hippocampal neurofunctional proteins, the ultrastructure of hippocampal nerves, and the histological changes in the hippocampus. After BMSCs treatment of RIBI, cognitive dysfunction improved significantly, the expression of hippocampal neurofunctional proteins was increased, the integrity of the hippocampal neural structure was protected, and nerve cell survival was enhanced. The improvement in neurological function was successfully detected by MEMRI, and MEMRI was highly correlated with cognitive function and histological changes. These results suggest that BMSCs treatment of RIBI is an optional modality, and MEMRI can be used for treatment evaluation.

Tunnel lining crack expansion and maintenance strategy optimization considering train loads: A case study.

Cracks in concrete tunnel linings are inevitable during service life. It is necessary to keep abreast of the cracking condition of the lining and formulate reasonable inspection and maintenance measures to ensure operational safety. Considering the influence of train loads on the safety and service performance of cracked linings, the expansion process of lining cracks and the maintenance strategy of tunnels during the service period was investigated. The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. The optimization analysis of lining crack expansion, detection, and maintenance was carried out for an operational railroad tunnel. Finally, an optimization analysis of lining crack expansion and maintenance was carried out in a railway tunnel. The results show that the stress intensity factor at the tip of the lining cracks is the same as the train load waveform; the magnitude of the stress intensity factor approximately satisfies the exponential function relationship with the depth of cracks; the fatigue service life of cracked lining is positively correlated with the cost of inspection and maintenance; the adoption of the necessary maintenance and the increase in the number of inspections and maintenance have a better economy while meeting the expectation of the service life. According to the Pareto solution set, the management can formulate the inspection and maintenance strategy based on the tunnel's expected life and maintenance budget.

Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms.

BACKGROUND: Long-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no validated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites associated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning algorithms. METHODS: In total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe subgroups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Machine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk. RESULTS: A total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely associated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970-0.979 on calibration set (n = 132) and 0.818-0.848 on validation set (n = 33). CONCLUSION: Altered metabolites and disrupted pathways are prevalent in arsenicosis patients; The disrupted metabolism of one carbon and dysfunction of antioxidant defense system may partially be causes of the systematic multi-organ damage and carcinogenesis in arsenicosis patients; Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of arsenicosis.

Scaling of polymer dynamics at an oil-water interface in regimes dominated by viscous drag and desorption-mediated flights.

Polymers are found near surfaces and interfaces in a wide range of chemical and biological systems, and the structure and dynamics of adsorbed polymer chains have been the subject of intense interest for decades. While polymer structure is often inferred from dynamic measurements in bulk solution, this approach has proven difficult to implement at interfaces, and the understanding of interfacial polymer conformation remains elusive. Here we used single-molecule tracking to study the interfacial diffusion of isolated poly(ethylene glycol) molecules at oil-water interfaces. Compared to diffusion in dilute aqueous solution, which exhibited the expected dependence of the diffusion coefficient (D) upon molecular weight (M) of D ∼ M(-1/2) for a Gaussian chain, the behavior at the interface was approximately D ∼ M(-2/3), suggesting a significantly more expanded polymer conformation, despite the fact that the oil was a poor solvent for the polymer. Interestingly, this scaling remained virtually unchanged over a wide range of oil viscosity, despite the fact that at low viscosities the magnitude of the diffusion coefficient was consistent with expectations based on viscous drag (i.e., Stokes-Einstein diffusion), and for high viscosity oil, the interfacial mobility was much faster than expected and consistent with the type of intermittent hopping transport observed at the solid-liquid interface. The dependence on molecular weight, in both regimes, was consistent with results from both self-consistent field theory and previous Monte Carlo simulations, suggesting that an adsorbed polymer chain adopted a partially swollen (loop-train-tail) interfacial conformation.

Nanoscale topography influences polymer surface diffusion.

Using high-throughput single-molecule tracking, we studied the diffusion of poly(ethylene glycol) chains at the interface between water and a hydrophobic surface patterned with an array of hexagonally arranged nanopillars. Polymer molecules displayed anomalous diffusion; in particular, they exhibited intermittent motion (i.e., immobilization and "hopping") suggestive of continuous-time random walk (CTRW) behavior associated with desorption-mediated surface diffusion. The statistics of the molecular trajectories changed systematically on surfaces with pillars of increasing height, exhibiting motion that was increasingly subdiffusive and with longer waiting times between diffusive steps. The trajectories were well-described by kinetic Monte Carlo simulations of CTRW motion in the presence of randomly distributed permeable obstacles, where the permeability (the main undetermined parameter) was conceptually related to the obstacle height. These findings provide new insights into the mechanisms of interfacial transport in the presence of obstacles and on nanotopographically patterned surfaces.

Molecular characterization of Salmonella enterica serovar Enteritidis on retail raw poultry in six provinces and two National cities in China.

One hundred and twenty six Salmonella Enteritidis isolates recovered from 1152 retail raw poultries were characterized by antimicrobial susceptibility test, pulsed-field gel electrophoresis (PFGE), presence of quinolone resistance (Qnr) associated genes, Class I integron, extended spectrum beta-lactamases (ESBLs) encoding genes, and mutations in quinolone resistance-determining region (QRDR) of GyrA and ParC. Resistance was most frequently found to nalidixic acid (88.1%), followed by to tetracycline (65.9%), sulfisoxazole (65.1%), and ampicillin (61.9%), and a less extent to cefoxitin (8.7%), gatifloxacin (8.7%), levofloxacin (7.9%), ceftriaxone (7.1%), and ceftiofur (6.3%). One hundred and twenty three (98.4%) isolates were resistant to at least one antibiotic, and 93 (74.4%) to at least four antibiotics. aac(6')-Ib-cr, qnrB, qnrA and qnrS genes were detected in 15 (11.9%), 11 (8.7%), 6 (4.8%) and 1 (0.8%) isolates, respectively. Amino acid substitutions of Ser83Tyr, Asp87Asn, Asp87Tyr, Asp87Gly and Ser83Phe/Asp87Asn were detected in QRDR of GyrA, Arg80Ser was the unique mutation in ParC. Eight isolates were detected with amino acid substitution both in GyrA and ParC. Three isolates carried Class I integron that harboring dfrA17-aadA5, dhfR1-aadA1, and dfrA1, respectively. Five isolates were detected carrying bla(TEM)-bla(ACC) (n = 1), bla(TEM) (n = 1), bla(TEM)-bla(OxA) (n = 3), respectively. Genetic diversities (D = 0.9255) were found among isolates based on PFGE analysis.