Frequency-dependent alterations in functional connectivity in patients with Alzheimer's Disease spectrum disorders.

Background: In the spectrum of Alzheimer's Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands. Methods: This investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC. Results: Significant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band. Conclusion: This study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function.

Brain entropy changes in classical trigeminal neuralgia.

Background: Classical trigeminal neuralgia (CTN) is a common and severe chronic neuropathic facial pain disorder. The pathological mechanisms of CTN are not fully understood. Recent studies have shown that resting-state functional magnetic resonance imaging (rs-fMRI) could provide insights into the functional changes of CTN patients and the complexity of neural processes. However, the precise spatial pattern of complexity changes in CTN patients is still unclear. This study is designed to explore the spatial distribution of complexity alterations in CTN patients using brain entropy (BEN). Methods: A total of 85 CTN patients and 79 age- and sex-matched healthy controls (HCs) were enrolled in this study. All participants underwent rs-fMRI and neuropsychological evaluations. BEN changes were analyzed to observe the spatial distribution of CTN patient complexity, as well as the relationship between these changes and clinical variables. Sixteen different machine learning methods were employed to classify the CTN patients from the HCs, and the best-performing method was selected. Results: Compared with HCs, CTN patients exhibited increased BEN in the thalamus and brainstem, and decreased BEN in the inferior semilunar lobule. Further analyses revealed a low positive correlation between the average BEN values of the thalamus and neuropsychological assessments. Among the 16 machine learning methods, the Conditional Mutual Information Maximization-Random Forest (CMIM-RF) method yielded the highest area under the curve (AUC) of 0.801. Conclusions: Our study demonstrated that BEN changes in the thalamus and pons and inferior semilunar lobule were associated with CTN and machine learning methods could effectively classify CTN patients and HCs based on BEN changes. Our findings may provide new insights into the neuropathological mechanisms of CTN and have implications for the diagnosis and treatment of CTN.

Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment.

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear. Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)). Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores. Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.

Static and dynamic functional connectivity combined with the triple network model in amnestic mild cognitive impairment and Alzheimer's disease.

Background: Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders. Methods: Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately. Results: Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores. Conclusion: It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.

Complexation and degradation of tetracycline by activation of molecular oxygen with biochar-supported nano-zero-valent copper composite.

Nano-zero-valent copper (nZVC) is a superior molecular oxygen (O2) activator for the abatement of organic pollutants due to its high electron utilization rate. However, the activation efficiency of O2 is compromised by the agglomeration tendency of nZVC particles and the concomitant reduction of the available active sites. To address this problem, the biochar (BC) with porous structure and abundant surface functional groups is utilized to disperse and stabilize nZVC for O2 activation (simplified as the nZVC/BC/O2 system) for efficient removal of tetracycline (TC). The nZVC/BC composite possesses a high specific area with well-distributed nZVC particles on the BC surface, which guarantees the superior dispersion and high reactivity in the activation of O2. The efficacy of the nZVC/BC/O2 system for TC abatement is evaluated and the underlying mechanism is elucidated. The results show that nZVC/BC/O2 system can achieve excellent removal of TC with the efficiencies of more than 85% in the pH range of 4.0-9.0, which originated from the combined action of complexation and degradation. The degradation is dominated by reactive oxygen species (ROS) including •OH, •O2- and 1O2 generated by Cu0/Cu+ activated O2 while the generation of Cu2+ via oxygen oxidation on the surface of nZVC/BC can remove TC by complexation adsorption. This study highlights the complexation and degradation in the removal of TC and can be expected to exhibit application prospects in the water and wastewater treatment.

Graphene Nanosheets as Lubricant Additives: Effects of Nature and Size on Lubricating Performance.

Graphene has been widely investigated as an additive in lubricating oils to enhance their tribological performance. Here, the effects of the nature and size of the graphene nanosheets on the tribological performance were investigated with the hydrogenated hydroxyl-terminated polybutadiene dioctoate (O-HHTPB-O) as a model base oil after alkylation of the graphene oxide (GO) of different sizes with 1-dodecylamine (DA) and reduction. The 1-dodecylamine-modified graphene oxide (DA-GO) showed better dispersibility in the O-HHTPB-O base oil and subsequently better tribological performance than the reduced one (DA-rGO) for both the larger graphene oxide nanosheets (GOL) and the smaller graphene oxide nanosheets (GOS). The DA-GOS exhibited better wear-reduction performance than the DA-GOL, owing to its smaller size and higher polarity. Although the DA-GOL could be ground during the friction, the friction and wear in the original period affected the complete period lubricating performance.

Groundwater remediation using Magnesium-Aluminum alloys and in situ layered doubled hydroxides.

In situ remediation of groundwater by zerovalent iron (ZVI)-based technology faces the problems of rapid passivation, fast agglomeration, limited range of pollutants and secondary contamination. Here a new concept of Magnesium-Aluminum (Mg-Al) alloys and in situ layered double hydroxides on is proposed for the degradation and removal of a wide variety of inorganic and organic pollutants from groundwater. The Mg-Al alloy provides the electrons for the chemical reduction and/or the degradation of pollutants while released Mg2+, Al3+ and OH- ions react to generate in situ LDH precipitates, incorporating other divalent and trivalent metals and oxyanions pollutants and further adsorbing the micropollutants. The Mg-Al alloy outperforms ZVI for treating acidic, synthetic groundwater samples contaminated by complex chemical mixtures of heavy metals (Cd2+, Cr6+, Cu2+, Ni2+ and Zn2+), nitrate, AsO33-, methyl blue, trichloroacetic acid and glyphosate. Specifically, the Mg-Al alloy achieves removal efficiency ≥99.7% for these multiple pollutants at concentrations ranging between 10 and 50 mg L-1 without producing any secondary contaminants. In contrast, ZVI removal efficiency did not exceed 90% and secondary contamination up to 220 mg L-1 Fe was observed. Overall, this study provides a new alternative approach to develop efficient, cost-effective and green remediation for water and groundwater.

Removal of 4-chlorophenol from polluted water by aluminum-iron alloys.

Chlorophenols are extremely toxic to the environment and recalcitrant to biological degradation. Herein chemical degradation of 4-chlorophenol (4-CP) from aqueous solutions by zero-valent aluminum (Al), zero-valent iron (Fe), Al and Fe mixtures (Al/Fe mass ratio 90/10, labeled as Al/Fe10) and Al-Fe alloy (Al/Fe mass ratio 90/10, labeled as Al-Fe10) were investigated. No removal was found for 50 mg·L-1 4-CP under anoxic conditions at initial pH 2.5 during a period of 10 hrs while 56%, 83%, 78% and 99% of 4-CP were removed by Fe, Al, Al/Fe10 and Al-Fe10, respectively under aeration conditions. The removal of 4-CP by Al/Fe10 mixtures was primarily in the Fe mode in the beginning 4 h and then transitioned to the Al mode. The removal of 4-CP by Al-Fe10 alloy was accomplished via two intermediate products, hydroquinone (HQ) and 4-chlorocatechol (4-CC), and it was speculated that reactive oxygen species and hydroxyl radicals (·OH) play an important role in the degradation of 4-CP and that Al-Fe intermetallic compounds might catalyze the reactions. This study demonstrates that alloying Al with Fe offers a promising strategy for developing new materials for water and wastewater remediation.

The effect of copper on iron reduction and its application to the determination of total iron content in iron and copper ores by potassium dichromate titration.

The International Standard Organization (ISO) specifies two titrimetric methods for the determination of total iron content in iron ores using potassium dichromate as titrant after reduction of the iron(III) by tin(II) chloride and/or titanium(III) chloride. These two ISO methods (ISO2597-1 and ISO2597-2) require nearly boiling-point temperature for iron(III) reduction and suffer from copper interference and/or mercury pollution. In this study, potassium borohydride was used for reduction of iron(III) catalyzed by copper ions at ambient temperatures. In the absence of copper, iron(III) reduction by potassium borohydride was sluggish while a trace amount of copper significantly accelerated the reduction and reduced potassium borohydride consumption. The catalytic mechanism of iron(III) reduction in sulfuric acid and hydrochloric acid was investigated. Potassium borohydride in sodium hydroxide solution was stable without a significant degradation within 24h at ambient conditions and the use of potassium borohydride prepared in sodium hydroxide solution was safe and convenient in routine applications. The applicability of potassium borohydride reduction for the determination of total iron content by potassium dichromate titration was demonstrated by comparing with the ISO standard method using iron and copper ore reference materials and iron ore samples.