The effect of platelet-rich plasma as a scaffold in regeneration/revitalization endodontics of immature permanent teeth assessed using 2-dimensional radiographs and cone beam computed tomography: a randomized controlled trial.

AIM: To assess the outcomes of platelet-rich plasma as a scaffold in regenerative/revitalization endodontics (RET) using cone beam computed tomography (CBCT) and 2-dimensional radiographs. METHODOLOGY: Twenty-six healthy patients with mean age of 12.66 ± 4.47, and immature permanent anterior teeth with necrotic pulps, were randomly allocated to two groups, whereby RET was performed using platelet-rich plasma (PRP, test group) and blood clot (BLC, control group). Changes in root length (RL), root dentinal thickness (RDT), apical foramen width (AFW) and radiographic root area (RRA), were assessed using both radiographic methods, whilst changes in periapical area diameter (PAD) were assessed using CBCT, over a period of 12 months. T-test and chi-square/Fisher's exact tests were used to compare continuous and categorical data between BLC and PRP groups, respectively. Changes in RL, RDT, AFW, RRA and PAD were examined by comparing the two groups (PRP versus BLC) using multilevel modelling, considering the clustering effect of repeated measures of several teeth originating from the same participant. RESULTS: Changes in RL, RDT, AFW, RRA and PAD, over time, were found to be significant for both groups. There was, however, no difference between the RET techniques (PRP versus BLC), using both radiographic and CBCT methods. The results of both assessment techniques (CBCT and 2-dimensional radiographic methods) were highly consistent (overall ICC ranged between 0.80 and 0.94). In addition, a significant effect of baseline PAD was found on RL, RRA and AD at 12 months (RL effect = -0.68, P < 0.001; RRA effect = -1.91, P = 0.025; AD effect = 0.08, P = 0.024). CONCLUSION: The current study highlights successful and comparable clinical and radiographic outcomes of RET techniques using PRP and BLC. Standardized and calibrated 2-dimensional radiographic assessment was as effective as CBCT in assessing RET outcomes; therefore, the routine use of CBCT in RET is not recommended. Although an effect of baseline periapical lesion diameter on root development outcomes, at 12 months, were observed, more studies are recommended in order to assess such an effect.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection.

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.

A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography.

We propose an automated, explainable artificial intelligence (xAI) system for age-related macular degeneration (AMD) diagnosis. Mimicking the physician's perceptions, the proposed xAI system is capable of deriving clinically meaningful features from optical coherence tomography (OCT) B-scan images to differentiate between a normal retina, different grades of AMD (early, intermediate, geographic atrophy (GA), inactive wet or active neovascular disease [exudative or wet AMD]), and non-AMD diseases. Particularly, we extract retinal OCT-based clinical imaging markers that are correlated with the progression of AMD, which include: (i) subretinal tissue, sub-retinal pigment epithelial tissue, intraretinal fluid, subretinal fluid, and choroidal hypertransmission detection using a DeepLabV3+ network; (ii) detection of merged retina layers using a novel convolutional neural network model; (iii) drusen detection based on 2D curvature analysis; (iv) estimation of retinal layers' thickness, and first-order and higher-order reflectivity features. Those clinical features are used to grade a retinal OCT in a hierarchical decision tree process. The first step looks for severe disruption of retinal layers' indicative of advanced AMD. These cases are analyzed further to diagnose GA, inactive wet AMD, active wet AMD, and non-AMD diseases. Less severe cases are analyzed using a different pipeline to identify OCT with AMD-specific pathology, which is graded as intermediate-stage or early-stage AMD. The remainder is classified as either being a normal retina or having other non-AMD pathology. The proposed system in the multi-way classification task, evaluated on 1285 OCT images, achieved 90.82% accuracy. These promising results demonstrated the capability to automatically distinguish between normal eyes and all AMD grades in addition to non-AMD diseases.

Muscle-derived cells for treatment of iatrogenic sphincter damage and urinary incontinence in men.

INTRODUCTION: Aim of this study was to assess the safety and efficacy of injection of autologous muscle-derived cells into the urinary sphincter for treatment of postprostatectomy urinary incontinence in men and to characterize the injected cells prior to transplantation. METHODS: 222 male patients with stress urinary incontinence and sphincter damage after uroloical procedures were treated with transurethral injection of autologous muscle-derived cells. The transplanted cells were investigated after cultivation and prior to application by immunocytochemistry using different markers of myogenic differentiation. Feasibility and functionality assessment was achieved with a follow-up of at least 12 months. RESULTS: Follow-up was at least 12 months. Of the 222 treated patients, 120 responded to therapy of whom 26 patients (12%) were continent, and 94 patients (42%) showed improvement. In 102 (46%) patients, the therapy was ineffective. Clinical improvement was observed on average 4.7 months after transplantation and continued in all improved patients. The cells injected into the sphincter were at least ~50% of myogenic origin and representative for early stages of muscle cell differentiation. CONCLUSIONS: Transurethral injection of muscle-derived cells into the damaged urethral sphincter of male patients is a safe procedure. Transplanted cells represent different phases of myogenic differentiation.

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World.

In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.

A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging.

Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-to-end convolution neural networks (i.e., ResNet50).

Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images.

This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system's performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.

A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice.

Cardiac magnetic resonance (MR) imaging is one of the most rigorous form of imaging to assess cardiac function in vivo. Strain analysis allows comprehensive assessment of diastolic myocardial function, which is not indicated by measuring systolic functional parameters using with a normal cine imaging module. Due to the small heart size in mice, it is not possible to perform proper tagged imaging to assess strain. Here, we developed a novel deep learning approach for automated quantification of strain from cardiac cine MR images. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of the LV cavity and myocardium via a novel FCN architecture. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. Furthermore, we demonstrated that our new algorithm can determine the strain differences between normal and diseased hearts.

EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS.

Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.

[Operative treatment of female urinary incontinence with particular consideration of sexuality according to anatomical findings]. / Operative Therapie der weiblichen Harninkontinenz unter Berücksichtigung sexualitätsrelevanter Strukturen.

An evaluation of surgical treatment modalities for urinary incontinence shows that loop plasties are easy to use and with good short-term results with regard to achievement of urinary continence. However, in the light of reports of serious complications and the lack of long-term results, this indication must be reconsidered. In the framework of prolapse surgery Burch's colposuspension with sarcopexy and, possibly, rectopexy represents the standard therapy. Laparoscopic procedures are currently being evaluated. Experimental concepts such as, for example, tissue engineering for the reconstruction of a damaged sphincter apparatus or the use of hyaluronic acid as a bulking agent are potential procedures for the treatment of stress incontinence that need investigation, especially since these methods will probably not have visible negative impacts of the female patient's sexual function. What we need is an interdisciplinary project like that currently practiced in the German Centre for Pelvic Surgery. Objectives are the preservation and reattainment for female sexuality in the course of operations to achieve urinary continence and other pelvic interventions.

Detection of microspheres in vivo using multispectral optoacoustic tomography.

We introduce a new approach to detect individual microparticles that contain NIR fluorescent dye by multispectral optoacoustic tomography in the context of the hemoglobin-rich environment within murine liver. We encapsulated a near infrared (NIR) fluorescent dye within polystyrene microspheres, then injected them into the ileocolic vein, which drains to the liver. NIR absorption was determined using multispectral optoacoustic tomography. To quantitate the minimum diameter of microspheres, we used both colorimetric and spatial information to segment the regions in which the microspheres appear. Regional diameter was estimated by doubling the maximum regional distance. We found that the minimum microsphere size threshold for detection by multispectral optoacoustic tomography images is 78.9 µm.

A comparison of two cause-of-death classification systems for deaths among women of reproductive age in Menoufia, Egypt.

Mortality data ascertained from sources other than a death registration system can validate the accuracy of the system, but this information is rarely obtained. Data on 1979 deaths among reproductive age women were collected in the 1981-1983 Reproductive Age Mortality Survey (RAMOS) in the governorate of Menoufia, Egypt, and compared with data on these deaths as recorded by the Egyptian death registration system. Although the distribution of the causes of death were similar, there were substantial differences between classification systems for deaths due to particular causes. Over half of the deaths classified differently by the systems were those assigned to circulatory disease on the death certificate. In contrast, there was a high rate of agreement between systems in the classification of trauma deaths. About half (52.4%) of cancer deaths had the same site-specific cancer diagnosis assigned by RAMOS. The percentage of deaths assigned to maternal causes was three times higher in RAMOS (19.2%) than on death certificates (6.1%). Reported mortality rates for this often-preventable cause of death have been substantially underestimated in national death registration systems. Such underreporting masks the need for additional prenatal care and maternal health programmes.

Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video.

AIM: Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition. METHODS: This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such as (i) lumen-intima shapes, (ii) bulb slopes, (iii) bulb curvature, (iv) mean lumen thickness and its variations, and (v) geometric shape fitting. RESULTS: Our database consists of 155 ultrasound bulb images taken from various ultrasound machines with varying resolutions and imaging conditions. Further we run our automated system blindly to spot out the bulbs in a mixture database of 336 images consisting of bulbs and no-bulbs. We are able to detect the bulbs in the bulb database with 100% accuracy having 92% as close as to a neurologists's bulb location. Our mean lumen-intima error is 0.0133 mm with precision against the manual tracings to be 98.92%. Our bulb detection system is fast and takes on an average 9 seconds per image for detection for the bulb edge points and 4 seconds for verification/validation of the bulb edge points.

Hepatocyte Lysosomal Membrane Stabilization by Olive Leaves against Chemically Induced Hepatocellular Neoplasia in Rats.

Extensive efforts are exerted looking for safe and effective chemotherapy for hepatocellular carcinoma (HCC). Specific and sensitive early biomarkers for HCC still in query. Present work to study proteolytic activity and lysosomal membrane integrity by hepatocarcinogen, trichloroacetic acid (TCA), in Wistar rats against aqueous olive leaf extract (AOLE).TCA showed neoplastic changes as oval- or irregular-shaped hepatocytes and transformed, vesiculated, and binucleated liver cells. The nuclei were pleomorphic and hyperchromatic. These changes were considerably reduced by AOLE. The results added, probably for the first time, that TCA-induced HCC through disruption of hepatocellular proteolytic enzymes as upregulation of papain, free cathepsin-D and nonsignificant destabilization of lysosomal membrane integrity, a prerequisite for cancer invasion and metastasis. AOLE introduced a promising therapeutic value in liver cancer, mostly through elevating lysosomal membrane integrity. The study substantiated four main points: (1) the usefulness of proteolysis and lysosomalmembrane integrity in early prediction of HCC. (2) TCA carcinogenesis is possibly mediated by lysosomal membrane destabilization, through cathepsin-D disruption, which could be reversed by AOLE administration. (3) A new strategy for management of HCC, using dietary olive leaf system may be a helpful phytotherapeutic trend. (4) A prospective study on serum proteolytic enzyme activity may introduce novel diagnostic tools.

Incidental finding of a large chest wall osteosarcoma--a case report.

Primary chest wall tumors are rare and primary osteosarcoma of the chest wall is considered as an even rare among the primary chest wall malignant tumors. The main presentation is rapidly expanding painful mass with elevation of alkaline phosphatase. We present a case of a 34 years old male who was found to have an incidental asymptomatic large chest wall mass with normal alkaline phosphatase level. He underwent several radiological diagnostic modalities which showed the extent and delineation of the mass. Complete excision of the mass was achieved and the chest wall defect was reconstructed with Prolene mesh. The histopathology confirmed the diagnosis of osteosarcoma of the chest wall.