Immobilization of tannin onto dialdehyde chitosan as a strategy for highly efficient and selective Au(III) adsorption.

Recycling of Au(III) from wastewater can not only increase resource utilization but also reduce environmental pollution. Herein, a chitosan-based bio-adsorbent (DCTS-TA) was successfully synthesized via crosslinking reaction between tannin (TA) and dialdehyde chitosan (DCTS) for the recovery of Au(III) from the solution. The maximum adsorption capacity for Au(III) was 1146.59 mg/g at pH 3.0, which fitted well with the Langmuir model. The XRD, XPS, and SEM-EDS analyses demonstrated that Au(III) adsorption on DCTS-TA was a collaborative process involving electrostatic interaction, chelation, and redox reaction. Existence of multiple coexisting metal ions did not significantly affect the Au(III) adsorption efficiency, with >90 % recovery of DCTS-TA obtained after five cycles. DCTS-TA is a promising candidate for Au(III) recovery from aqueous solutions due to its easy preparation, environmental-friendliness, and high efficiency.

Value of Magnetic Resonance Diffusion Tensor Imaging Combined with Quantitative Electroencephalogram in Diagnosis of Neurocognitive Impairment in Patients with White Matter Demyelination.

This paper aimed to explore the clinical value of combined adoption of magnetic resonance diffusion tensor imaging (DTI) and quantitative electroencephalogram (QEEG) in assessing microstructure changes and mild neurocognitive dysfunction in patients with white matter demyelination. 128 cases of white matter demyelination admitted to the hospital from October 2018 to October 2019 were rolled into the research group, and 100 healthy patients physically examined during the same period were rolled into the control (ctrl) group. QEEG and magnetic resonance DTI examinations were performed for all patients. The wave power of δ, θ, α, and ß and the ratio of α/θ and (δ + θ)/(α + ß) were recorded. The FA values of white matter fibers in different brain areas were measured, and the Montreal Cognitive Assessment (MoCA) and Addenbrooke Cognitive Evaluation rating (ACE-R) were adopted to assess the neurocognitive function of patients. It was found that the dominant frequency of each brain area in the research group was 8-9 Hz slow α wave. In contrast with the ctrl, the α wave and α/θ values in the research group were lower, while θ wave and δ + θ/α + ß values were higher (P < 0.05); the scores of ACE-R and MoCA were lower (P < 0.01); the fractional anisotropy (FA) values of the right frontal lobe white matter (0.335 ± 0.068), the left temporal lobe white matter (0.391 ± 0.032), and the corpus callosum knee white matter (0.658 ± 0.053) were lower (P < 0.05). The FA values of these three areas were positively correlated with attention and calculation, memory, and memory of MoCA scale, respectively (P < 0.05). The FA value of the right frontal white matter was positively correlated with the attention and calculation score of the ACE-R scale (P < 0.05). In conclusion, magnetic resonance DTI combined with QEEG could reflect the microstructural changes of white matter, which may be associated with mild neurocognitive impairment. The primary objective of the study was to explore the clinical value of combined adoption of magnetic resonance DTI and QEEG in assessing microstructure changes and mild neurocognitive dysfunction in patients with white matter demyelination, expected to provide a theoretical basis for the treatment of white matter demyelination.

[Combination of apparent diffusion coefficient and amplitude-integrated electroencephalogram to predict the outcome of comatose patients with severe traumatic brain injury].

OBJECTIVE: To examine whether the combination of quantitative regional apparent diffusion coefficient (ADC) and amplitude-integrated electroencephalogram (aEEG) can predict the outcome of comatose patients with severe traumatic brain injury (sTBI). METHODS: A prospective study was conducted. The patients with coma caused by sTBI [Glasgow coma scale (GCS) < 8] admitted to Suqian First Hospital from January 2016 to June 2019 were enrolled. All patients underwent aEEG examination and magnetic resonance imaging (MRI) scan within 1 week after emergency treatment. The ADC values of 9 regions of interest (frontal gray matter and white matter, parietal gray matter and white matter, temporal gray matter and white matter, caudate nucleus of basal ganglia, lenticular nucleus and thalamus) were measured by head MRI, and the mean ADC values of frontal lobe, parietal lobe, temporal lobe and basal ganglia were calculated respectively. According to the follow-up results after 12 months, the differences of each index between patients with poor prognosis [Glasgow outcome score (GOS) 1-2] and patients with good prognosis (GOS 3-5) were compared; the receiver operating characteristic curve (ROC curve) was drawn to evaluate the predictive ability of aEEG and ADC for the good prognosis of patients with sTBI, and the predictive value of the combination of aEEG and ADC. RESULTS: A total of 52 patients with sTBI were enrolled, with mean age of (36.7±13.9) years old, 35 of whom were male. Within 12 months follow-up, 29 patients had achieved favorable outcomes and 23 patients had unfavorable outcome. There were 21, 17 and 14 patients with aEEG, and grade, respectively, and 19, 10 and 0 patients had good prognosis respectively. ADC values of 9 regions of interest in patients with good prognosis were significantly higher than those in patients with poor prognosis (×10-6 mm2/s: 924±107 vs. 531±87 in frontal gray matter, 804±95 vs. 481±74 in frontal white matter, 831±93 vs. 683±72 in temporal gray matter, 726±87 vs. 654±63 in temporal white matter, 767±79 vs. 690±75 in parietal gray matter, 716±84 vs. 642±62 in parietal white matter, 689±70 vs. 465±68 in caudate nucleus, 723±84 vs. 587±71 in lenticular nucleus, 807±79 vs. 497±67 in thalamus, all P < 0.01). ROC curve analysis showed that the area under ROC curve (AUC) of aEEG for predicting good prognosis of sTBI patients was 0.826, when the cut-off value of aEEG was < 1.5, the sensitivity was 94.7% and the specificity was 72.8%. Among the ADC value prediction abilities in the interested areas, the prediction of ADC value in frontal lobe and basal ganglia area were better than that in sTBI patients. AUC was 0.817 and 0.903 respectively. The best cut-off values were > 726×10-6 mm2/s and > 624×10-6 mm2/s respectively, the sensitivity of predicting prognosis were both 100%, and the specificity was 63.4% and 61.8%. A model combining frontal ADC and basal ganglia ADC with aEEG was 91.0% sensitive and 93.7% specific for favorable outcome of sTBI patients. CONCLUSIONS: Combination of the quantitative measurement of regional ADC and aEEG may be useful for predicting the outcome of the patients with sTBI.