Diseased Area Research Articles

Scheimpflug Backscatter Imaging of the Fibrillar Layer in Fuchs Endothelial Corneal Dystrophy

Retrospective analysis of a prospective observational case series. A total of 34 eyes (34 subjects) undergoing Descemet membrane endothelial keratoplasty (DMEK) surgery with preoperative high-quality Scheimpflug backscatter imaging data were included. The Descemet endothelium complex (DEC) was retrieved during DMEK surgery, and immunofluorescence staining was performed for collagens I, III, and IV. The FL morphology in en face Scheimpflug backscatter and immunofluorescence imaging was compared and agreement of FL parameters was analyzed using intraclass correlation coefficients (ICC) and Bland-Altman plots. Scheimpflug backscatter imaging delineated the FL in 26 eyes and was FL negative in 8 eyes with deviation compared to immunofluorescence in 1 case and good agreement of morphology characteristics. Horizontal caliper diameter ± SD was 4.84 ± 0.85 mm, vertical caliper diameter was 3.92 ± 0.78 mm, maximum caliper diameter was 5.12 ± 0.82 mm, and surface area was 12.43 ± 4.74 mm2. Compared to immunofluorescence imaging, mean difference (95% limits of agreement) and intraclass correlation coefficients were for horizontal caliper diameter 0.13 mm (-0.81 to 1.1 mm) and 0.88, vertical caliper diameter 0.23 mm (-0.76 to 1.2 mm) and 0.81, maximum caliper diameter 0.06 mm (-1.1 to 1.2 mm) and 0.86, and surface area 1.4 mm2 (-3.9 to 6.7 mm2) and 0.85. Scheimpflug backscatter imaging facilitates visualization of the FL in advanced FECD eyes, offering the potential to identify particularly diseased areas of the FECD endothelium in vivo.

Investigating targeted spring dead spot management via aerial mapping and precision‐guided fungicide applications

AbstractSpring dead spot (Ophiosphaerella herpotricha, O. korrae, O. narmari; SDS) is among the most destructive diseases of bermudagrass [Cynodon dactylon (L.) Pers.] and hybrid bermudagrass (C. dactylon x C. transvaalensis Burtt Davy) in the Transition Zone of the United States. Spring dead spot's primary causal agents in the United States, Ophiosphaerella korrae and O. herpotricha, infect bermudagrass in the fall with symptoms appearing in the spring when winter dormancy breaks. Patches of necrotic turfgrass often reoccur in the same location and expand into surrounding areas. Chemical control options are often expensive or provide inconsistent results. Our objectives were to develop SDS incidence maps, analyze these maps, and evaluate suppression efficacy of chemical applications guided by incidence‐based maps. Digital imagery captured with an unmanned aerial vehicle (UAV) was used to create SDS incidence maps in the spring of 2016, 2017, and 2018. In the fall of 2016 and 2017, a targeted, site‐specific penthiopyrad treatment was evaluated against blanket, full‐coverage applications of penthiopyrad and tebuconazole, and a nontreated control. Treatments were compared using digital image analysis of diseased area (DA) and post‐treatment SDS patch count (PC). Across both metrics, the penthiopyrad treatments had significantly less disease than both the tebuconazole and nontreated control in 2016–2017. Targeted penthiopyrad compared favorably to full coverage penthiopyrad for DA and PC in 2016–2017, but full‐coverage penthiopyrad was superior to targeted penthiopyrad and tebuconazole in 2017–2018 for both DA and PC. Targeted penthiopyrad using SDS incidence maps required 51% less fungicides in 2016–2017 and 65% less in 2017–2018 when compared to full‐coverage penthiopyrad.

Study of the relationship between acupuncture dose and effect

Study of the relationship between acupuncture dose and effect

Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accuracy. An user oriented graphical user interface is created for understanding the automated system.

Leaf Image-Based Plant Disease Identification Using Color and Texture Features

Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on tenfold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.

Computer Modeling of the Heart for ECG Interpretation—A Review

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.

PLANT DISEASE RECOGNITION AND CLUSTERING USING FUZZY ALGORITHM ON DATA MINING

Deep learning models are capable of performing sophisticated calculations, but they''''re not suitable for mobile and handheld devices due to their vast size and demanding computational requirements. Using an automated process, we intend to identify the diseases of plants so that we can build a process which begins with pre-processing, separates diseased leaf area, calculates features based on the Gray-Level Co-occurrence Matrix (GLCM), chooses and classifies features, and ends with decision making. Through threshold segmentation, we were able to isolate the diseased leaf areas in the maize plants, and then use this information to create fuzzy decision rules for the assignment of images of Common Rust to its severity class. These results were obtained with six colour and texture features. Plant disease clustering is performed with the Fuzzy Algorithm. The measurements show higher performance to the conventional methodologies and are ranked highest in terms of feature extraction method. This suggests that leaf-based plant disease diagnosis is the most appropriate method. These capabilities can be considered by the addition of new disease classifications or specific crop or disease classifications.

A deep semantic segmentation correction network for multi-model tiny lesion areas detection

BackgroundSemantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task.MethodsWe propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas.ResultsIn our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI.ConclusionsOverall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.

Detection of Disease in Leaf using Artificial Neural Network

Leaf spots are diseased areas on leaves. Agricultural production is negatively affected by plant diseases. We have proposed a system that can help farmers identify leaf diseases and take appropriate preventive measures to increase crop yields. We take images of diseased leaves and perform various pre-processing techniques on them, to detect the edges of the leaves. The entire region of interest is divided into blocks, and then the characteristics of each block are compared with the characteristics of the image in the database. The main purpose is to identify the disease in the leaf spot of the crop as 80-90% of the plant diseases occur in the leafspot. ANN methods are used for the classification of plant diseases. Using these methods, we can accurately identify and classify various plant diseases. We will create an end-to-end Android application. It is possible to run applications on mobile devices through the use of convolution operations, special integration, and the computational efficiency of using ANN to share parameters. This project will be of great use to the farmers as it will help them to detect the plant diseases in the early stage and enhance the production of crop.

Flying Squirrel-Inspired Motion Control of a Light-Deformed Pt-PAzoMA Micromotor through Drag Force Manipulation.

Micromotors require stable and precise motion control for complex tasks such as microsurgery, drug delivery in vivo, or environmental monitoring ex vivo. However, a continuous control signal is needed for micromotors to achieve motion control during their whole journey, which hinders their application in areas where external control stimuli are limited or unavailable. Fortunately, nature suggests an excellent solution that flying squirrel exhibits motion tuning capability by deforming itself when jumping off a tall tree. Inspired by this, we propose a Pt-PAzoMA Janus micromotor that precisely changes its shape (from a spherical shape to an elliptical shape) under a brief light signal (450 nm) and maintains this deformation until next signal reception. The deformed elliptical micromotor performs relatively low-speed motion compared to the spherical one, which is further confirmed by massive simulation results. In addition, by investigating motion behavior experimentally and theoretically, it is proved that the motion modulation is caused by the drag force changing brought from the deformation. This method represents a different route to regulate the motion of micromotors without a continuous signal, which is useful in application scenarios where the environmental control signal is inaccessible/limited or long-time operation with minimum energy input is required to maintain motion manipulation. With further function modification, this kind of shape-changing micromotor has potential in optimizing drug diffusion efficiency by speed altering and long-term monitoring at the diseased area by confining the active range of the micromotor in the targeted area through deformation.

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups.

Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

Review on Microspheres as Drug Carriers for Controlled Drug Delivery

Microspheres are organic or inorganic spherical particles with a diameter of 1–1000 μm. These materials can encapsulate drugs or bioactive molecules and release them in a controlled way. Biodegradable polymers are frequently used for the development of microsphere matrixes such as polylactic acid and copolymer of lactic acid and glycolic acid. Apart from them, there is an extensive range of microspheres prepared from albumin, albumin dextran sulfate, and fibrinogen. Administration of medication via micro particulate systems is advantageous because microspheres can be ingested or injected; they can be tailored for desired release profiles and used for site-specific delivery of drugs and in some cases can even provide organ-targeted release. Microspheres in drug delivery are used for targeted as well as prolonged drug release in the diseased area. It also protects the unstable or pH-sensitive drugs before and after the administration. In this review we have discussed about advantages, disadvantages, methods of preparations and evaluation and types of microspheres. Keywords: Microsphere, Biodegradable polymers, Polymer Microsphere, Drug Release

The Role of Polymer Carrier and Process Parameters for Small Molecule Drug Delivery via Blended Electrospinning

Delivery of therapeutic compounds to the diseased area in the body with minimized adverse effects is the underlying objective behind development of advanced drug delivery systems. Providing disease-specific release patterns is the ultimate goal of any drug delivery system. Electrospinning has been widely used for nanofiber fabrication. Having high aspect ratio and similarity to the extracellular matrix in the body make electrospun nanofibers a great candidate to be used as drug delivery implants. In this study, we report electrospinning to be a tunable technique capable of providing engineered, disease-specific drug release patterns. Using “one factor at a time” and “central composite design” techniques, we respectively demonstrate flow rate and applied voltage to be the two most significant parameters (with [Formula: see text]-values of 512.48 and 42.31) affecting the final fiber diameter, and capillary-to-collector distance as the least important one, by evaluating their influence, individually and combined, on the morphology of electrospun Poly (Lactide-co-Glycolide acid) nanofibers. Using the same two techniques, we also show that hydrophobicity of the polymeric fibrous scaffold, measured by water contact angle (WCA) with the [Formula: see text]-value of 376.44, is the main factor to consider when designing an electrospun fibrous drug delivery system for a specific disease, while fiber diameter can further modulate the release pattern of the drug from hydrophobic polymeric nanofibers. We finally support our hypothesis by comparing our findings with analysis of data derived from the literature. Taken together, our findings suggest electrospinning to be a tunable technique capable of providing various release patterns for any small molecular weight drug on the basis of the requirements of the diseases to be treated.

Characteristics of high-resolution magnetic resonance vessel wall imaging of cervicocerebral artery dissection and the influential factors of vascular recanalization.

Cervicocerebral artery dissection (CAD) is one of the important causes for ischemic stroke in young and middle-aged people. CAD is dangerous and untimely diagnosis and treatment are likely to result in severe disability. Early diagnosis and timely intervention can greatly improve the prognosis of patients. This study was to investigate the imaging features of CAD on high-resolution magnetic resonance vessel wall imaging (HRMR-VWI) and to analyze the influential factors of vascular recanalization. A total of 19 CAD patients with both baseline HRMR-VWI and follow-up data of vascular imaging in the period from April 2017 to December 2019 in Department of Radiology, Xiangya Hospital, Central South University were retrospectively analyzed. The diseased vessels were divided into a recovery group and a unrecovered group. After treatment, diseased vessels with no residual arterial dissection and no residual stenosis in the lumen were included in the recovery group. Diseased vessels with stenosis, occlusion or residual dissection were included in the unrecovered group. Diseased vessels were divided into a ischemic stroke group and a non-ischemic stroke group according to the presence or absence of ischemic stroke in the area supplied by the diseased vessels. Differences in clinical data and HRMR-VWI imaging findings were compared between the groups. A total of 26 vessels were involved, including 14 (53.8%) internal carotid artery extracranial segment, 8 (30.8%) vertebral artery extracranial segment, 3 (11.5%) vertebral artery intracranial segment, and 1 (3.9%) basilar artery. Ischemic stroke occurred in 16 diseased vascular supply areas. Intramural hematoma was all observed in the baseline HMR-VWI of the affected vessels. There were 18 vessels (69.2%) in the recovery group and 8 vessels (30.8%) in the unrecovered group. Compared with the vessels in the recovery group, the vessels in the unrecovered group were mostly found in the intracranial segment (P<0.05). However, there were no significant differences in gender, age, imaging findings of dissection, length of lesion involvement, time between reexamination, vascular occlusion, and antiplatelet therapy between the recovery group and the unrecovered group (all P>0.05).There were nosignificant differences in age, gender, complication of hypertension, hyperlipidemia, diabetes mellitus, lesion location, vascular occlusion, lesion involvement length, double lumen sign, internal membrane, and lumen thrombosis between the ischemic stroke group and the non-ischemic stroke group (all P>0.05). Intramural hematoma is a common imaging manifestation of CAD and can be shown clearly and accurately on HRMR-VWI. Recanalization rate of CAD is high, and the recanalization of CAD in intracranial segment is slower than that of CAD in extracranial segment, which can prolong the review time.

Citrus disease detection and classification using end-to-end anchor-based deep learning model

Plant diseases are the primary issue that reduces agricultural yield and production, causing significant economic losses and instability in the food supply. In plants, citrus is a fruit crop of great economic importance, produced and typically grown in about 140 countries. However, citrus cultivation is widely affected by various factors, including pests and diseases, resulted in significant yield and quality losses. In recent years, computer vision and machine learning have been widely used in plant disease detection and classification, which present opportunities for early disease detection and bring improvements in the field of agriculture. Early and accurate detection of plant diseases is crucial to reducing the disease’s spread and damage to the crop. Therefore, this paper employs a two-stage deep CNN model for plant disease detection and citrus diseases classification using leaf images. The proposed model consists of two main stages; (a) proposing the potential target diseased areas using a region proposal network; (b) classification of the most likely target area to the corresponding disease class using a classifier. The proposed model delivers 94.37% accuracy in detection and an average precision of 95.8%. The findings demonstrate that the proposed model identifies and distinguishes between the three different citrus diseases, namely citrus black spot, citrus bacterial canker and Huanglongbing. The proposed model serves as a useful decision support tool for growers and farmers to recognize and classify citrus diseases.

Detection of Diseases in Tomato Leaves by Color Analysis

Agricultural productivity is an important factor for the economic development of a country. Therefore, the diagnosis of plant diseases is a field of research of utmost importance for the agricultural sector as it allows us to help recommend strategies to avoid the spread of diseases, thus reducing economic losses. Currently, with the rise of computer systems, computer systems have been developed that allow computer-assisted diagnosis in different research fields, including the agricultural sector. This work proposes the development of a methodology that allows the detection of three types of diseases in tomato leaves (late blight, tomato mosaic virus and Septoria leaf spot) by image analysis and pattern recognition. The methodology is divided into three stages: (1) segmentation of the leaf and of the lesion, (2) feature extraction using color moments and Gray Level Co-occurrence Matrix (GLCM) and (3) classification. For the segmentation process, it is proposed to use a range of pixel colors that represent healthy and diseased areas in tomato leaves using values proposed by an expert in the area of phytopathology. For the classification it is proposed to use a decision rule in which if two of the Support Vector Machines (SVM) classifiers, K Nearest Neighbors (K-NN) and Multilayer Perceptron (MLP) give the same result, then this is taken for the final decision. The result of the methodology is compared with other classifiers using the value of its accuracy and validated with cross validation.

Magnetic Helical Hydrogel Motor for Directing T Cell Chemotaxis

AbstractMicro/nanomotors are attracting booming research enthusiasm with their revolutionary potential in biomedicine, sensing, and nanoengineering. Among the motors proposed, magnetic micro/nanomotors are of great interest with their high controllability and field biocompatibility. Yet the fabrication of magnetic actuated especially helical motors requires expensive and complicated instruments, 3D printing or glancing angle deposition, etc. Here, a soft and biocompatible helical poly(vinyl alcohol) (PVA) hydrogel motor via a versatile set‐up is engineered. The obtained helical hydrogel motor offers high capacity for chemokine CXCL12 and superparamagnetic iron oxide (Fe3O4) nanoparticles, which can then allow magnetic manipulation. With a low strength rotating magnetic field, the system is able to perform 3D precision navigation, necessary to steer the robotic system to a model diseased area. The chemokine cues from the hydrogel motor, acting as the synthetic leader cell, then directs immune T cell chemotactic migration. In a previously reported cell manipulating motor system, towing or pushing a single/two cell was demonstrated, with limited efficiency. This motor platform represents a novel approach for directing endogenous cell chemotaxis and organizing immune response.

Differential Responses of Cucurbita pepo to Podosphaera xanthii Reveal the Mechanism of Powdery Mildew Disease Resistance in Pumpkin.

Powdery mildew is one of the most destructive diseases and the major cause to the production losses of cucurbit worldwide. A number of strategies have been developed and applied to discover suitable and safer methods to manage the occurrence of powdery mildew disease in pumpkins (Cucurbita pepo L.), but information is limited in screening tolerant germplasms and exploring their mechanisms in preventing the disease occurrence at physiological, biochemical, and molecular levels. Therefore, we investigated the responses of two commercial pumpkin cultivars to Podosphaera xanthii infection. Compared with mock-inoculated seedlings, few small and sparse powdery areas were observed on the leaves of the Sixing F1 cultivar on the 13th day after inoculation with P. xanthii, whereas a large number of diseased powdery areas and a layer of white powdery mildew were observed on the surface of Jin12 F1 leaves. The inoculation duration (7, 9, 11, and 13 days) significantly and continuously increased the disease incidence and index of pumpkin seedlings. The contents of H2O2, MDA, lignin, and total phenolics in the leaves of Sixing F1 and Jin12 F1 cultivars were markedly increased after inoculation with P. xanthii. However, the Sixing F1 cultivar exhibited much less reactive oxygen species (ROS) accumulation, a lower rate of lipid peroxidation, and a higher level of lignin and total phenolics contents after inoculation than the Jin12 F1 cultivar. Compared with untreated control pumpkin seedlings, significantly higher activities and gene expressions of the phenylpropanoids pathway enzymes (PAL and PPO), ROS scavenging defense enzymes (SOD, CAT, POD, and APX), and other salicylic acid (SA) signaling pathway marker genes were observed in the leaves of both cultivars after P. xanthii inoculation at different inoculation time points. These enhancements were significantly higher in Sixing F1 than Jin12 F1. Our results indicate that the Sixing F1 cultivar exhibited a much stronger ability in resistance to P. xanthii infection than the Jin12 F1 cultivar. Our results suggest that one possible mechanism of C. pepo cultivars to prevent the pathogen P. xanthii infection is by activating and enhancing the activity and gene expression of the phenylpropanoids pathway to synthesize phenolic substances and lignin, ROS scavenging defense enzymes to eliminate the harmful effects of ROS, and signaling pathway marker gene expression to improve plant disease resistance.

Ileal Crohn's Disease Exhibits Similar Transmural Fibrosis Irrespective of Phenotype.

INTRODUCTION:In Crohn's disease (CD), the assessment of transmural inflammation and fibrosis is of utmost importance. This study aimed to quantify these parameters in CD ileal specimens and correlate them with disease progression.METHODS:This is a retrospective unicentric study based on the analysis of archived specimens (n = 103) of primary ileal resection. Data were retrieved from a prospective national inflammatory bowel disease registry. Two pathologists, blinded for CD phenotype and clinical indications for surgery, examined 3 sections per patient and graded inflammation and fibrosis, based on a histopathological score.RESULTS:Penetrating (B3, n = 74) CD exhibited significantly higher inflammation in diseased areas, compared with stricturing (B2, n = 29) disease (score 3: 96% vs 76%, P = 0.005 in inflamed areas; 78% vs 55%, P = 0.019 in most affected areas). This was also observed for the comparison of B2 CD with B3 CD with (B3s, n = 54) and without associated stricture (B3o, n = 20): B3s vs B2: 81% vs 55%, P = 0.033 in most affected areas; B3o vs B2: 100% vs 76%, P = 0.006 in inflamed areas; 70% vs 55%, P = 0.039 in most affected areas. We could not show differences in fibrosis scores between the subphenotypes. Postoperative new penetrating events occurred only in B3s (n = 6, 11%, P = 0.043) patients. The changing of biologic therapy after surgery correlated with severe inflammation at the proximal ileal margin (55% changed vs 25% not changed, P = 0.035).DISCUSSION:In our cohort, fibrosis scores and fibromuscular changes were comparable, irrespective of CD phenotype. Inflammation severity was the major differentiator between penetrating and stricturing disease.

Current status and future of delivery systems for prevention and treatment of infections in the oral cavity.

Oral health reflects the general health, and it is fundamental to well-being and quality of life. An infection in the oral cavity can be associated with serious complications in human health. Local therapy of these infections offers many advantages over systemic drug administration, targeting directly to the diseased area while minimizing systemic side effects. Specialized drug delivery systems into the oral cavity have to be designed in such a fashion that they resist to the aqueous environment that is constantly bathed in saliva and subject to mechanical forces. Additionally, a prolonged release of drug should also be provided, which would enhance the efficacy and also decrease the repeated dosing. This review is aimed to summarize the current most relevant findings related to local drug delivery of various drug groups for prevention and treatment of infections (viral, bacterial, fungal) and infection-related manifestations in the oral cavity. Current therapeutic challenges in regard to effective local drug delivery systems will be discussed, and the recent approaches to overcome these obstacles will be reviewed. Finally, future prospects will be overviewed to promote novel strategies that can be implemented in clinical management for prevention and treatment of oral infections.Graphical abstract