A potential brain functional biomarker distinguishing patients with Crohn’s disease with different disease stages: a resting-state fMRI study

BackgroundThe previous studies have demonstrated that patients with Crohn’s disease in remission (CD-R) have abnormal alterations in brain function. However, whether brain function changes in patients with Crohn’s disease in activity (CD-A) and the relationship with CD-R are still unclear. In this study, we aimed to investigate whether the different levels of disease activity may differentially affect the brain function and to find the brain functional biomarker distinguishing patients with different disease stages by measuring the amplitude of low frequency fluctuations (ALFF).Methods121 patients with CD and 91 healthy controls (HCs) were recruited. The clinical and psychological assessment of participants were collected. The criteria for the disease activity were the Crohn’s disease activity index (CDAI) scores. CD-R refers to CD patients in remission which the CDAI score is less than 150. Conversely, CD-A refers to CD patients in activity which the CDAI score is ≥150. The ALFF was compared among three groups by performing one-way analysis of variance, followed by a post hoc two-sample t-test. Differences among the groups were selected as seeds for functional connectivity analyses. We also investigated the correlation among clinical, psychological scores and ALFF. Binary logistic regression analysis was used to examine the unique contribution of the ALFF characteristics of the disease stages.ResultsThere were widespread differences of ALFF values among the 3 groups, which included left frontal pole (FP_L), right supramarginal gyrus (SG_R), left angular gyrus (AG_L), right cingulate gyrus (CG_R), right intracalcarine cortex (IC_R), right parahippocampal gyrus (PG_R), right lingual gyrus (LG_R), right precuneous cortex (PC_R), left occipital fusiform gyrus (OFG_L). Significant brain regions showing the functional connections (FC) increased in FP_L, SG_R, PC_R and OFG_L between CD-A and HCs. The erythrocyte sedimentation rate had a negative correlation with the ALFF values in PC_R in the patients with CD. The phobic anxiety values had a negative correlation with the ALFF values in OFG_L. The psychoticism values had a negative correlation with ALFF values in the IC_R. And the hostility values had a positive correlation with the ALFF values in CG_R. Significant brain regions showing the FC increased in FP_L, SG_R, CG_R, PG_R, LG_R and OFG_L between CD-R and HCs. In binary logistic regression models, the LG_R (beta = 5.138, p = 0.031), PC_R (beta = 1.876, p = 0.002) and OFG_L (beta = 3.937, p = 0.044) was disease stages predictors.ConclusionThe results indicated the significance of the altered brain activity in the different disease stages of CD. Therefore, these findings present a potential identify neuroimaging-based brain functional biomarker in CD. Additionally, the study provides a better understanding of the pathophysiology of CD

Host Defense Peptides as Effector Molecules of the Innate Immune Response: A Sledgehammer for Drug Resistance?

Host defense peptides can modulate the innate immune response and boost infection-resolving immunity, while dampening potentially harmful pro-inflammatory (septic) responses. Both antimicrobial and/or immunomodulatory activities are an integral part of the process of innate immunity, which itself has many of the hallmarks of successful anti-infective therapies, namely rapid action and broad-spectrum antimicrobial activities. This gives these peptides the potential to become an entirely new therapeutic approach against bacterial infections. This review details the role and activities of these peptides, and examines their applicability as development candidates for use against bacterial infections

Sap Transporter Mediated Import and Subsequent Degradation of Antimicrobial Peptides in Haemophilus

Antimicrobial peptides (AMPs) contribute to host innate immune defense and are a critical component to control bacterial infection. Nontypeable Haemophilus influenzae (NTHI) is a commensal inhabitant of the human nasopharyngeal mucosa, yet is commonly associated with opportunistic infections of the upper and lower respiratory tracts. An important aspect of NTHI virulence is the ability to avert bactericidal effects of host-derived antimicrobial peptides (AMPs). The Sap (sensitivity to antimicrobial peptides) ABC transporter equips NTHI to resist AMPs, although the mechanism of this resistance has remained undefined. We previously determined that the periplasmic binding protein SapA bound AMPs and was required for NTHI virulence in vivo. We now demonstrate, by antibody-mediated neutralization of AMP in vivo, that SapA functions to directly counter AMP lethality during NTHI infection. We hypothesized that SapA would deliver AMPs to the Sap inner membrane complex for transport into the bacterial cytoplasm. We observed that AMPs localize to the bacterial cytoplasm of the parental NTHI strain and were susceptible to cytoplasmic peptidase activity. In striking contrast, AMPs accumulated in the periplasm of bacteria lacking a functional Sap permease complex. These data support a mechanism of Sap mediated import of AMPs, a novel strategy to reduce periplasmic and inner membrane accumulation of these host defense peptides

An Active Contour Model Based on Retinex and Pre-Fitting Reflectance for Fast Image Segmentation

In the present article, this paper provides a method for fast image segmentation for computer vision, which is based on a level set method. One dominating challenge in image segmentation is uneven illumination and inhomogeneous intensity, which are caused by the position of a light source or convex surface. This paper proposes a variational model based on the Retinex theory. To be specific, firstly, this paper figures out the pre-fitting reflectance by using an algorithm in the whole image domain before iterations; secondly, it reconstructs the image domain using an additive model; thirdly, it uses the deviation between the global domain and low-frequency component to approximate the reflectance, which is the significant part of an energy function. In addition, a new regularization term has been put forward to extract the vanishing gradients. Furthermore, the new regularization term is capable of accelerating the segmentation process. Symmetry plays an essential role in constructing the energy function and figuring out the gradient flow of the level set

Application of an Electrochemical Immunosensor with a MWCNT/PDAA Modified Electrode for Detection of Serum Trypsin

Objective: To establish an electrochemical immunosensor for the determination of serum trypsin levels using a multiwall carbon nanotubes (MWCNTs)-composite-modified electrode. Method: A MWCNT composite coated on the surface of bare gold electrodes was used for fixation of an anti-trypsin antibody. The assembly process and the performance indicators, including sensitivity, linear range of detection, anti-jamming performance, and stability, of the electrochemical immunosensor were examined by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Results: With optimized experimental conditions, the difference of the current value measured by differential pulse voltammetry (DPV) showed a linear relationship with the concentration of serum trypsin within 0.10–100 ng/mL. The detection limit for trypsin using this sensor was 0.002 ng/mL. Conclusions: The electrochemical immunosensor built using the MWCNT-composite-modified electrode is simple to operate and has a fast response time, along with a wide linear range, high sensitivity, and accuracy, making it suitable for serum trypsin detection

An overview of intelligent image segmentation using active contour models

The active contour model (ACM) approach in image segmentation is regarded as a research hotspot in the area of computer vision, which is widely applied in different kinds of applications in practice, such as medical image processing. The essence of ACM is to make use ofuse an enclosed and smooth curve to signify the target boundary, which is usually accomplished by minimizing the associated energy function by means ofthrough the standard descent method. This paper presents an overview of ACMs for handling image segmentation problems in various fields. It begins with an introduction briefly reviewing different ACMs with their pros and cons. Then, some basic knowledge in of the theory of ACMs is explained, and several popular ACMs in terms of three categories, including region-based ACMs, edge-based ACMs, and hybrid ACMs, are detailedly reviewed with their advantages and disadvantages. After that, twelve ACMs are chosen from the literature to conduct three sets of segmentation experiments to segment different kinds of images, and compare the segmentation efficiency and accuracy with different methods. Next, two deep learning-based algorithms are implemented to segment different types of images to compare segmentation results with several ACMs. Experimental results confirm some useful conclusions about their sharing strengths and weaknesses. Lastly, this paper points out some promising research directions that need to be further studied in the future