Effect of a combination of donepezil tablets and butylphthalide soft capsules on neurological function in dementia patients, and its effect on serum inflammatory factors

Purpose: To determine the effect of combined use of donepezil tablets and butylphthalide soft capsules in the treatment of patients with vascular dementia, and its effect on serum inflammatory factor levels and neurological functional recovery of patients.Methods: 120 patients with vascular dementia were selected and assigned to group A (n = 60) and group B (n = 60). All patients were treated with donepezil tablets, while patients in group A were, in addition, treated with butylphthalide soft capsules. Mini mental state examination (MMSE) scores, clinical dementia rating scale (CDRS) scores, activities of daily living (ADL) scores, incidence of adverse reactions, serum inflammatory factor levels and neurological functional recovery were determined.Results: There was significantly higher MMSE score in group A than in B, while CDRS score was lower in group A. The ADL scores and inflammatory factor levels were lower in group A than in B (p < 0.001), while neurological functional recovery was markedly better in A (p < 0.001). Incidents of unwanted events were comparable in groups A and B, and there were no serious complications in the patients.Conclusion: The combination therapy of donepezil tablets and butylphthalide soft capsules reduces inflammatory factor levels and improved cognitive level and quality of life of patients with vascular dementia. It also produces good neurological functional recovery and low incidence of adverse reactions. Therefore, this treatment strategy has potentials for the management of vascular dementia

Analysis of a Single Hemodialysis on Phosphate Removal of the Internal Fistula Patients by Mathematical and Statistical Methods

Chronic kidney disease related mineral and bone disease (CKD-MBD) is a worldwide challenge in hemodialysis patients. In china, the number of dialysis patients is growing but few data are available about their bone disorders. In the current study, we aimed to evaluate the effect of clinical factors on the serum phosphorus clearance in the 80 maintenance hemodialysis (MHD) patients. Six clinical factors were identified for their association with the serum phosphorus clearance using the analysis of Spearman’s single linear correlation, including predialysis serum phosphate level, CRR, membrane surface area of the dialyzer, effective blood flow rate, the blood chamber volume, and hematocrit. In an overall multivariate analysis, pre-P, CRR, membrane SA, and Qb were identified as independent risk factors associated with the serum phosphorus clearance. In conclusion, HD could effectively clear serum phosphorus. The analysis of CRR might help to estimate serum phosphorus reduction ratio

Investigation of Microstructure Evolution and Phase Selection of Peritectic Cuce Alloy During High-Temperature Gradient Directional Solidification

In this work, a CuCe alloy was prepared using a directional solidification method at a series of withdrawal rates of 100, 25, 10, 8, and 5 μm/s. We found that the primary phase microstructure transforms from cellular crystals to cellular peritectic coupled growth and eventually, changes into dendrites as the withdrawal rate increases. The phase constituents in the directionally solidified samples were confirmed to be Cu2Ce, CuCe, and CuCe + Ce eutectics. The primary dendrite spacing was significantly refined with an increasing withdrawal rate, resulting in higher compressive strength and strain. Moreover, the cellular peritectic coupled growth at 10 μm/s further strengthened the alloy, with its compressive property reaching the maximum value of 266 MPa. Directional solidification was proven to be an impactful method to enhance the mechanical properties and produce well-aligned in situ composites in peritectic systems

Association between organic cation transporter genetic polymorphisms and metformin response and intolerance in T2DM individuals: a systematic review and meta-analysis

BackgroundVariants in organic cation transporter (OCT) genes play a crucial role in metformin pharmacokinetics and are critical for diabetes treatment. However, studies investigating the effect of OCT genetic polymorphisms on metformin response have reported inconsistent results. This review and meta-analysis aimed to evaluate the associations between OCT genetic polymorphisms and metformin response and intolerance in individuals with type 2 diabetes mellitus (T2DM).MethodA systematic search was conducted on PubMed, EMBASE, CNKI, WANFANG DATA, and VIP database for identifying potential studies up to 10 November 2022. The Q-Genie tool was used to evaluate the quality of included studies. Pooled odds ratios (OR) or standardized mean differences (SMD) and 95% confidence intervals (95% CI) were calculated to determine the associations between OCT genetic polymorphisms and metformin response and intolerance that were reflected by glycemic response indexes, such as glycated hemoglobin level (HbA1c%) or change in glycated hemoglobin level (ΔHbA1c%), fasting plasma level (FPG) or change in fasting plasma glucose level (ΔFPG), the effectiveness rate of metformin treatment, and the rate of metformin intolerance. A qualitative review was performed for the variants identified just in one study and those that could not undergo pooling analysis.ResultsA total of 30 related eligible studies about OCT genes (SLC22A1, SLC22A2, and SLC22A3) and metformin pharmacogenetics were identified, and 14, 3, and 6 single nucleotide polymorphisms (SNPs) in SLC22A1, SLC22A2, and SLC22A3, respectively, were investigated. Meta-analysis showed that the SLC22A1 rs622342 polymorphism was associated with a reduction in HbA1c level (AA vs. AC: SMD [95% CI] = −0.45 [−0.73–−0.18]; p = 0.001). The GG genotype of the SLC22A1 rs628031 polymorphism was associated with a reduction in FPG level (GG vs. AA: SMD [95 %CI] = −0.60 [−1.04–0.16], p = 0.007; GG vs. AG: −0.45 [−0.67–0.20], p < 0.001). No statistical association was found between the remaining variants and metformin response and intolerance.ConclusionSLC22A1 rs622342 and rs628031 polymorphisms were potentially associated with glycemic response to metformin. This evidence may provide novel insight into gene-oriented personalized medicine for diabetes

Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients

Effect of childhood maltreatment on cognitive function and its relationship with personality development and social coping style in major depression disorder patients: A latent class model and network analysis

Study objectivesThe study aimed to (1) analyze the interrelationships among different types of childhood adversity, diverse personality dimensions, and individual coping style integratively among major depressive disorder (MDD) patients and healthy participants using a network approach; (2) explore the latent class of child maltreatment (CM) and its relationship with cognitive function.MethodsData were collected from the Objective Diagnostic Markers and Personalized Intervention in MDD Patients (ODMPIM) study, including 1,629 Chinese participants. Using the Childhood Trauma Questionnaire to assess CM, the Simplified Coping Style Questionnaire to measure individual coping style, Eysenck Personality Questionnaire Revised-Short Form for personality characters, and a series of neurocognitive tests, including seven tests with 18 subtests for cognitive assessments. We used the “Network Module” in Jeffreys’s Amazing Statistics Program (JASP) and R package for network analysis. A latent class analysis was performed with SAS9.4.ResultsChild maltreatment was more common in MDD patients than in healthy controls, except for emotional abuse. Network analysis showed that emotional abuse, emotional neglect, physical abuse, and physical neglect formed quadrangle connections. Personality dimensions were associated with physical neglect and emotional abuse. All types of CM (excluding sex abuse) showed an association with coping style. Emotional neglect showed the highest centrality measures. Physical neglect had a high level of closeness. To a concerning strength, emotional and physical neglect showed the highest levels. The structure of the networks is variant between groups (M = 0.28, P = 0.04). Latent class analysis (LCA) revealed that three classes provided the best fit statistics. Neglect and abuse classes tended to perform more poorly on the five cognitive domains.ConclusionThis study provided insights on multi-type of CM. Neglect played an important role in different routes through the relation between CM with personality traits and social coping style. However, neglect has often been ignored in previous studies and should receive more public attention

Enantioselective electrochemical carbon-chloride bond cleavage of hexachlorocyclohexanes (HCHs) catalyzed by Mn (III) Cl-phthalocyanine

A lipophilic and electron-rich metallophthalocyanine Mn(III)Cl(α,α′-n-OC5H11)8Pc has been synthesized and characterized. A series of electrochemical experiments demonstrate that the Mn(III)Cl(α,α′-n-OC5H11)8Pc complex can be used as a catalyst for highly efficient carbon-chloride bond cleavage of environmental toxic hexachlorocyclohexanes (HCHs) through electrochemical catalysis, and that the increased catalytic efficiency is related to the enantiomeric carbon-chloride σ-bond of HCHs

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process