Optimization of the assessment of cerebral autoregulation in neurocritical care unit

Introduction Cerebral autoregulation (CA) refers to the physiological mechanisms in the brain to maintain constant blood flow despite changes in cerebral perfusion pressure (CPP). It plays an important protective role against the danger of ischaemia or oedema of the brain. Over the years, various methods for CA assessment have been proposed, while most commonly used parameters include the autoregulation index (ARI), which grades CA into ten levels; transfer function (TF) analysis, describing CA as a high pass filter; the mean flow index (Mx), that estimates CA through the correlation coefficient between slow waves of mean cerebral blood flow velocity (CBFV) and CPP; and pressure reactivity index (PRx), calculated as a moving correlation coefficient between mean arterial blood pressure (ABP) and intracranial pressure (ICP). However, until now, how these parameters are related with each other is still not clear. A comprehensive investigation of the relationship between all these parameters is therefore needed. In addition, the methods mentioned above mostly assume the system being analysed is linear and the signals are stationary, with the announcement of non-stationary characteristic of CA, a more robust method, in particular suitable for non-stationary signal analysis, needs to be explored. Objectives and Methods This thesis addresses three primary questions: 1. What are the relationships between currently widely used CA parameters, i.e. Mx, ARI, TF parameters, from theoretical and practical point of view? 2. It there an effective method that can be introduced to assess CA, which is suitable for analyses of non-stationary signals? 3. How can bedside monitoring of cerebral autoregulation be improved in traumatic brain injury patients? These general aims have been translated into a series of experiments, retrospective analyses and background studies that are presented in different chapters of this thesis. Results and Conclusions This PhD project carefully scrutinised currently used CA assessment methodologies in TBI patients, demonstrating significant relationships between ARI, Mx and TF phase. A new introduced wavelet-transform-based method, wPRx was validated and showed more stable result for CA assessment than the well-established parameter, PRx. A multi-window approach with weighting system for optimal CPP estimation was described. The result showed a significant improvement in the continuity and stability of CPPopt estimation, which made it possible to be applied in the future clinical management of TBI patients.Gates Cambridge Scholarshi

Analysis of the expression profile of Dickkopf-1 gene in human glioma and the association with tumor malignancy

<p>Abstract</p> <p>Background</p> <p>Gliomas represent the most common primary malignant brain tumors, yet little is known about the molecular pathogenesis of these tumors. The highly-regulated Wnt signal transduction pathway is essential for normal developmental processes, and defects in the pathway are closely linked to oncogenesis. Dickkopf-1 (DKK-1) is a secreted protein that acts as a potent inhibitor of the Wnt pathway. The aim of this study was to examine the expression profile of DKK-1 gene in human glioma and its association with tumor malignancy.</p> <p>Methods</p> <p>We determined the expression levels of DKK-1 transcript and protein in 12 glioblastoma cell lines, medulloblastoma cells, low-grade glioma cells, and human astrocyte cells by semiquantitative RT-PCR and ELISA. A total of 47 tumor biopsy specimens and 11 normal brain tissue samples from patients with cerebral trauma internal decompression were embedded in paraffin blocks and used for immunostaining. Twenty-six primary tumors and 7 corresponding brain samples were stored in liquid nitrogen and used for RT-PCR. We further examined serologic concentrations and cerebral fluid levels of DKK-1 in patients with tumors.</p> <p>Results</p> <p>DKK-1 could only be detected in 12 human glioblastoma cell lines, not in a panel of other tumor and normal cell lines. The difference between glioma patients and healthy individuals was significant. Kendall's tau-c association analysis also revealed the increased DKK-1 protein expression in tumor tissues of higher pathologic classification. The levels of cerebral fluid DKK-1 protein were significantly higher in glioma patients than in healthy donors or in neuronal benign tumor patients, suggesting that the DKK-1 molecule in cerebral fluids can be applicable to detect the presence of glioma and be developed as a novel prognostic treatment.</p> <p>Conclusion</p> <p>The Wnt antagonist DKK-1 gene may have important roles in glioma tumorigenesis and act as a novel biomarker in human malignant glioblastoma.</p

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging

Acousticelectric brain imaging (ABI), which is based on the acoustoelectric (AE) effect, is a potential brain function imaging method for mapping brain electrical activity with high temporal and spatial resolution. To further enhance the quality of the decoded signal and the resolution of the ABI, the decoding accuracy of the AE signal is essential. An adaptive decoding algorithm based on Fourier fitting (aDAF) is suggested to increase the AE signal decoding precision. The envelope of the AE signal is first split into a number of harmonics by Fourier fitting in the suggested aDAF. The least square method is then utilized to adaptively select the greatest harmonic component. Several phantom experiments are implemented to assess the performance of the aDAF, including 1-source with various frequencies, multiple-source with various frequencies and amplitudes, and multiple-source with various distributions. Imaging resolution and decoded signal quality are quantitatively evaluated. According to the results of the decoding experiments, the decoded signal amplitude accuracy has risen by 11.39% when compared to the decoding algorithm with envelope (DAE). The correlation coefficient between the source signal and the decoded timing signal of aDAF is, on average, 34.76% better than it was for DAE. Finally, the results of the imaging experiment show that aDAF has superior imaging quality than DAE, with signal-to noise ratio (SNR) improved by 23.32% and spatial resolution increased by 50%. According to the experiments, the proposed aDAF increased AE signal decoding accuracy, which is vital for future research and applications related to ABI

Social Preference Deficits in Juvenile Zebrafish Induced by Early Chronic Exposure to Sodium Valproate

Prenatal exposure to sodium valproate (VPA), a widely used anti-epileptic drug, is related to a series of dysfunctions, such as deficits in language and communication. Clinical and animal studies have indicated that the effects of VPA are related to the concentration and to the exposure window, while the neurobehavioral effects of VPA have received limited research attention. In the current study, to analyze the neurobehavioral effects of VPA, zebrafish at 24 hours post-fertilization (hpf) were treated with early chronic exposure to 20 μM VPA for 7 hours per day for 6 days or with early acute exposure to 100 μM VPA for 7 hours. A battery of behavioral screenings was conducted at 1 month of age to investigate social preference, locomotor activity, anxiety and behavioral response to light change. A social preference deficit was only observed in animals with chronic VPA exposure. Acute VPA exposure induced a change in the locomotor activity, while chronic VPA exposure did not affect locomotor activity. Neither exposure procedure influenced anxiety or the behavioral response to light change. These results suggested that VPA has the potential to affect some behaviors in zebrafish, such as social behavior and the locomotor activity, and that the effects were closely related to the concentration and the exposure window. Additionally, social preference seemed to be independent from other simple behaviors

Family and Individual Risk and Protective Factors of Depression among Chinese Migrant Children with Oppositional Defiant Disorder Symptoms

Migrant children reached 35.81 million in China and were vulnerable to serious emotional problems including depression. The present study aimed to identify the family and individual risk and protective factors for depression in an at-risk sample of Chinese migrant children. Participants were 368 children (9.47 ± 1.46 years old, 73.4% boys) who had at least one symptom of Oppositional Defiant Disorder symptoms (ODD) and their parents in Mainland China. Risk and protective factors within both family (i.e., family maltreatment and family functioning) and individual (i.e., automatic thoughts and resilience) perspectives. Family maltreatment and negative automatic thoughts served as risk factors in relation to children's depression. Further, automatic thoughts mediated the relationship between family maltreatment and children's depression. Family functioning (cohesion, but bot adaptability) and individual resilience could buffer the effects of risk factors in the Structure Emotion Model such that both cohesion and resilience moderated the relationship between family maltreatment and children's automatic thoughts only. Our findings highlighted the urgent need to decrease risk factors and increase protective factors of both family and child individual characteristics in prevention and intervention depression among migrant children with ODD symptoms in China

UPLC-MS/MS method for Icariin and metabolites in whole blood of C57 mice: development, validation, and pharmacokinetics study

Icariin, a Chinese medicinal herb with significant effects on Alzheimer’s disease, lacks pharmacokinetic data in mice. To address this, a UPLC-MS/MS method was developed and validated for quantifying Icariin and its metabolites, Icariside I and Icariside II, in the whole blood of mice. The method processed micro-whole blood from serial collections of the same C57 mouse, with well-fitted linearity (0.25–800 ng mL−1) and intra- and inter-day precision and accuracy within 15%. Short-time and autosampler stability were verified, with acceptable extraction recoveries and matrix effects over 74.55%. After intravenous administration (15 mg kg−1) of Icariin in C57 mice, Icariside I and Icariside II were detected within 2 min. However, after the intragastric administration (30, 90, and 150 mg kg−1) of Icariin in C57 mice, Icariin and Icariside I were not detected, and Icariin was rapidly converted into Icariside II. Furthermore, the Cmax and AUC0-t of three doses (30, 90, and 150 mg kg-1) of Icariside II increased as the dose increased. In conclusion, this method improves the traditional method of collecting only one blood sample from each mouse, detecting Icariin and its metabolites in the whole blood of mice, especially for serial collection of micro-whole blood

Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation

The MBD7 complex promotes expression of methylated transgenes without significantly altering their methylation status.

DNA methylation is associated with gene silencing in eukaryotic organisms. Although pathways controlling the establishment, maintenance and removal of DNA methylation are known, relatively little is understood about how DNA methylation influences gene expression. Here we identified a METHYL-CpG-BINDING DOMAIN 7 (MBD7) complex in Arabidopsis thaliana that suppresses the transcriptional silencing of two LUCIFERASE (LUC) reporters via a mechanism that is largely downstream of DNA methylation. Although mutations in components of the MBD7 complex resulted in modest increases in DNA methylation concomitant with decreased LUC expression, we found that these hyper-methylation and gene expression phenotypes can be genetically uncoupled. This finding, along with genome-wide profiling experiments showing minimal changes in DNA methylation upon disruption of the MBD7 complex, places the MBD7 complex amongst a small number of factors acting downstream of DNA methylation. This complex, however, is unique as it functions to suppress, rather than enforce, DNA methylation-mediated gene silencing

Enhancive effects of Lewis y antigen on CD44-mediated adhesion and spreading of human ovarian cancer cell line RMG-I

<p>Abstract</p> <p>Background</p> <p>This study aimed to investigate the molecular structural relationship between cell adhesive molecule CD44 and Lewis y antigen, and determine the effects of Lewis y antigen on CD44-mediated adhesion and spreading of ovarian cancer cell line RMG-I and the Lewis y antigen-overexpressed cell line RMG-I-H.</p> <p>Methods</p> <p>The expression of CD44 in RMG-I and RMG-I-H cells before and after treatment of Lewis y monoclonal antibody was detected by immunocytochemistry; the expression of Lewis y antigen and CD44 was detected by Western Blot. The structural relationship between Lewis y antigen and CD44 was determined by immunoprecipitation and confocal laser scanning microscopy. The adhesion and spreading of RMG-I and RMG-I-H cells on hyaluronic acid (HA) were observed. The expression of CD44 mRNA in RMG-I and RMG-I-H cells was detected by real-time RT-PCR.</p> <p>Results</p> <p>Immunocytochemistry revealed that the expression of CD44 was significantly higher in RMG-I-H cells than in RMG-I cells (<it>P </it>< 0.01), and its expression in both cell lines was significantly decreased after treatment of Lewis y monoclonal antibody (both <it>P </it>< 0.01). Western Blot confirmed that the content of CD44 in RMG-I-H cells was 1.46 times of that in RMG-I cells. The co-location of Lewis y antigen and CD44 was confirmed by co-immunoprecipitation. The co-expression of CD44 and Lewis y antigen in RMG-I-H cells was 2.24 times of that in RMG-I cells. The adhesion and spreading of RMG-I-H cells on HA were significantly enhanced as compared to those of RMG-I cells (<it>P </it>< 0.01), and this enhancement was inhibited by Lewis y monoclonal antibody (<it>P </it>< 0.01). The mRNA level of CD44 in both cell lines was similar (<it>P </it>> 0.05).</p> <p>Conclusion</p> <p>Lewis y antigen strengthens CD44-mediated adhesion and spreading of ovarian cancer cells.</p