A systematic review of inherited retinal dystrophies in Pakistan: updates from 1999 to April 2023.

BACKGROUND: Inherited retinal degenerations (IRDs) are a group of rare genetic conditions affecting retina of the eye that range in prevalence from 1 in 2000 to 1 in 4000 people globally. This review is based on a retrospective analysis of research articles reporting IRDs associated genetic findings in Pakistani families between 1999 and April 2023. METHODS: Articles were retrieved through survey of online sources, notably, PubMed, Google Scholar, and Web of Science. Following a stringent selection criterion, a total of 126 research articles and conference abstracts were considered. All reported variants were cross-checked and validated for their correct genomic nomenclature using different online resources/databases, and their pathogenicity scores were explained as per ACMG guidelines. RESULTS: A total of 277 unique sequence variants in 87 distinct genes, previously known to cause IRDs, were uncovered. In around 70% cases, parents of the index patient were consanguineously married, and approximately 88.81% of the detected variants were found in a homozygous state. Overall, more than 95% of the IRDs cases were recessively inherited. Missense variants were predominant (41.88%), followed by Indels/frameshift (26.35%), nonsense (19.13%), splice site (12.27%) and synonymous change (0.36%). Non-syndromic IRDs were significantly higher than syndromic IRDs (77.32% vs. 22.68%). Retinitis pigmentosa (RP) was the most frequently observed IRD followed by Leber's congenital amaurosis (LCA). Altogether, mutations in PDE6A gene was the leading cause of IRDs in Pakistani families followed by mutations in TULP1 gene. CONCLUSION: In summary, Pakistani families are notable in expressing recessively inherited monogenic disorders including IRDs likely due to the highest prevalence of consanguinity in the country that leads to expression of rare pathogenic variants in homozygous state.

Effect of Short-Term Ageing Treatment on Bending Force Behavior of Commercial Nickel-Titanium Archwire.

Superelastic nickel-titanium (NiTi) archwires have become the preferred archwire for orthodontic alignment and the levelling stage due to their ability to exert a light force on teeth throughout a wide range of tooth movement. The magnitude and trend of the force exerted on the malposed tooth is influenced by the orthodontist's consideration of the size and geometry of the NiTi archwire during orthodontic therapy. In this work, a novel approach of a short-term ageing treatment was utilized to modify the magnitude and trend of the bending force of a commercial superelastic NiTi archwire. The bending behavior of the superelastic NiTi archwire was altered by subjecting it to different temperatures in an ageing treatment for 15 min. The bending behavior of the aged NiTi archwire was examined using a three-point and three-bracket setup. The commercial NiTi archwire's bending forces in both the three-point and three-bracket configurations were successfully altered by the 15 min ageing treatment. During unloading in the three-bracket arrangement, the NiTi archwires aged at 490 °C or 520 °C exhibited a lower magnitude and more consistent force compared to the NiTi archwires aged at 400 °C or 430 °C. Ageing the archwire for 15 min at 490 °C produced a suitable size of Ni4Ti3 precipitate, which makes the wire more flexible during bending and reduces the unloading force in the three-bracket bending configuration. The short-term aged NiTi archwire could be used to enhance the force delivery trend to the malposed tooth by lowering the amplitude of the force delivered and sustaining that force throughout the orthodontic treatment duration.

Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation.

This paper presents a novel two-stage image segmentation method using an edge scaled energy functional based on local and global information for intensity inhomogeneous image segmentation. In the first stage, we integrate global intensity term with a geodesic edge term, which produces a preliminary rough segmentation result. Thereafter, by taking final contour of the first stage as initial contour, we begin second stage segmentation process by integrating local intensity term with geodesic edge term to get final segmentation result. Due to the suitable initialization from the first stage, the second stage precisely achieves desirable segmentation result for inhomogeneous image segmentation. Two stage segmentation technique not only increases the accuracy but also eliminates the problem of initial contour existed in traditional local segmentation methods. The energy function of the proposed method uses both global and local terms incorporated with compacted geodesic edge term in an additive fashion which uses image gradient information to delineate obscured boundaries of objects inside an image. A Gaussian kernel is adapted for the regularization of the level set function and to avoid an expensive re-initialization. The experiments were carried out on synthetic and real images. Quantitative validations were performed on Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2015 and PH2 skin lesion database. The visual and quantitative comparisons will demonstrate the efficiency of the proposed method.

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours.

Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.