Epidemiology of cutaneous leishmaniasis in children of Khyber Pakhtunkhwa, Pakistan.

OBJECTIVES: In Pakistan, cutaneous leishmaniasis is an emerging tropical disease and a very high number (>70%) of children are afflicted by this marring infection. This study aimed to scrutinise the prevalence, spatial distribution and socio-demographic and behavioural risk factors associated with cutaneous leishmaniasis in children aged <5-15 years in Khyber Pakhtunkhwa. METHODS: A total of 1, 559 clinically confirmed records of children diagnosed with cutaneous leishmaniasis (January-December) from 2020 and 2022 were obtained from selected district hospitals. In addition, a risk factors-related questionnaire was administered to 1, 011 households (400 in 2020 and 611 in 2022) in nine districts during a household survey. RESULTS: The maximum number of cutaneous leishmaniasis cases was recorded in 2022 (n = 877, 56.25%) as compared to 2020 (n = 682, 43.75%). The hospital records showed a greater number of male patients in the 2022 cohort (n = 603, 68.76%). The highest number of cases were observed in children aged 5-9 years in 2022 (n = 282, 32.16%) and 2020 (n = 255, 37.39%). In 2020 and 2022, cutaneous leishmaniasis cases showed peak aggregation in March (n = 118, 17.3%) and January (n = 322, 36.72%). From a spatial analysis, the maximum number of cutaneous leishmaniasis cases was recorded at 59-1700 m elevation in various land-use/land-cover and climatic regions with quaternary alluvium rock formations. A multivariate logistic regression model analysis of risk factors from the households survey suggested that age group, socio-economic status, construction materials of the house, use of insect repellents, Afghan refugee camps in the village/district, knowledge and biting times of sand flies, frequent use of mosquito bed nets, presence of domestic animals in the house, knowledge of the transmission period and peak month of leishmaniasis infection increased the risk of acquiring cutaneous leishmaniasis (p value < 0.05). CONCLUSION: Our analysis demonstrated that cutaneous leishmaniasis in children is influenced by a variety of environmental, socio-demographic and behavioural risk factors in Khyber Pakhtunkhwa. The increase in recorded cases of cutaneous leishmaniasis in children in 2022 compared to 2020 suggests that the infection likely extended to new foci in the province.

A new Gaussian curvature of the image surface based variational model for haze or fog removal.

Outdoor images are usually affected by haze which limits the visibility and reduces the contrast of the images. Removal of haze from real-world images is always a challenging task. Recently, many mathematical models have been proposed for the effective removal of haze from real-world images. However, these models may produce staircase effects or lower the image contrast or smooth the edges of the object. In this paper, we propose a model based on Gaussian curvature for the de-hazing of images. The atmospheric veil estimate is included based on dark channel prior (DCP), which can significantly reduce the artifacts on the edge of the image and increase the accuracy. The transmission map then changes to a high-quality map to reduce haze or fog from gray and color images. DCP combined with Gaussian curvature is done for the first time for image de-hazing/de-fogging. The augmented Lagrangian method is used to find the minimizer of the proposed functional, which will be a system of partial differential equations. To get fast convergence, fast Fourier transforms (FFT) is used to solve the system of PDEs. The performance of the proposed model is compared with other state-of-the-art models qualitatively and quantitatively. The proposed model is tested on various real and synthetic images which show better efficiency in staircase effects reduction, haze/fog removal, image contrast, corners, and sharp edges conservation respectively.

Power mean based image segmentation in the presence of noise.

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements.

Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.

Novel Missense CNTNAP2 Variant Identified in Two Consanguineous Pakistani Families With Developmental Delay, Epilepsy, Intellectual Disability, and Aggressive Behavior.

We report the genetic analysis of two consanguineous pedigrees of Pakistani ancestry in which two siblings in each family exhibited developmental delay, epilepsy, intellectual disability and aggressive behavior. Whole-genome sequencing was performed in Family 1, and we identified ~80,000 variants located in regions of homozygosity. Of these, 615 variants had a minor allele frequency ≤ 0.001, and 21 variants had CADD scores ≥ 15. Four homozygous exonic variants were identified in both affected siblings: PDZD7 (c.1348_1350delGAG, p.Glu450del), ALG6 (c.1033G>C, p.Glu345Gln), RBM20 (c.1587C>G, p.Ser529Arg), and CNTNAP2 (c.785G>A, p.Gly228Arg). Sanger sequencing revealed co-segregation of the PDZD7, RBM20, and CNTNAP2 variants with disease in Family 1. Pathogenic variants in PDZD7 and RBM20 are associated with autosomal recessive non-syndromic hearing loss and autosomal dominant dilated cardiomyopathy, respectively, suggesting that these variants are unlikely likely to contribute to the clinical presentation. Gene panel analysis was performed on the two affected siblings in Family 2, and they were found to also be homozygous for the p.Gly228Arg CNTNAP2 variant. Together these families provide a LOD score 2.9 toward p.Gly228Arg CNTNAP2 being a completely penetrant recessive cause of this disease. The clinical presentation of the affected siblings in both families is also consistent with previous reports from individuals with homozygous CNTNAP2 variants where at least one allele was a nonsense variant, frameshift or small deletion. Our data suggests that homozygous CNTNAP2 missense variants can also contribute to disease, thereby expanding the genetic landscape of CNTNAP2 dysfunction.

Characterization of sand fly breeding sites in district Malakand, Khyber Pakhtunkhwa, Pakistan, and evaluation of risk factors for cutaneous leishmaniasis in the region.

Present study was carried to determine the sand fly species composition, breeding sites ecology, seasonal abundance, and spatial distribution in district Malakand, Khyber Pakhtunkhwa, Pakistan. In addition, risk factors associated with cutaneous leishmaniasis (CL) were also evaluated. Survey of indoor and outdoor habitats was carried out using sticky traps in 31 villages of Dargai and Batkhela tehsils of Malakand. Soil from habitats of adult and immature sand flies was analysed. Questionnaire-based household survey was also performed in these villages to assess risk factors associated with CL. Soil samples from selected CL positive households were analysed for its contents. Additionally, clinicoepidemiological data from local health centres was examined for the year 2019. Total of 3,140 sand flies belonging to 18 species were collected. Phlebotomus sergenti was the most abundant species (38.16%). Its abundance had a strong positive correlation with mean monthly relative humidity and negative correlation with average temperature. Phlebotomus sergenti and Phlebotomus papatasi were abundant at an elevation ranging from 320 to 1,120 m above sea level and in agricultural lands near human settlements. Flight height preference apparatus collected maximum sand flies at 30 cm (1ft) above the ground and all species associated negatively with height. Soil analysis from habitats of adult and immature flies showed that highest mean number of adults and immatures were recorded from silt loam which carried highest concentrations of K2 O, Mg, Ca, and Zn. Number of immature sand flies correlated moderately (r = .7, p < .05) with K2 O soil concentrations. There was significant similarity between organic matter contents in soil samples from positive breeding sites and CL households (Wilcoxon rank-sum test, p = .1976). In multivariate analysis model for CL risk factors, age (26-35 and >35 years), knowledge of leishmaniasis, living in a middle and upper class, preachers visit to villages, and assumption that Afghan refugees are more prone to CL were significant. CL patient's archived data from health centres showed that majority of patients had lesions on face and hands. Patient's influx was highest in February and March.

Image Segmentation for Intensity Inhomogeneity in Presence of High Noise.

Automated segmentation of fine objects details in a given image is becoming of crucial interest in different imaging fields. In this paper, we propose a new variational level-set model for both global and interactive\selective segmentation tasks, which can deal with intensity inhomogeneity and the presence of noise. The proposed method maintains the same performance on clean and noisy vector-valued images. The model utilizes a combination of locally computed denoising constrained surface and a denoising fidelity term to ensure a fine segmentation of local and global features of a given image. A two-phase level-set formulation has been extended to a multi-phase formulation to successfully segment medical images of the human brain. Comparative experiments with state-of-the-art models show the advantages of the proposed method.

Sn doping induced enhancement in the activity of ZnO nanostructures against antibiotic resistant S. aureus bacteria.

Highly ionic metal oxide nanostructures are attractive, not only for their physiochemical properties but also for antibacterial activity. Zinc oxide (ZnO) nanostructures are known to have inhibitory activity against many pathogens but very little is known about doping effects on it. The antibacterial activity of undoped ZnO and tin (Sn) doped ZnO nanostructures synthesized by a simple, versatile, and wet chemical technique have been investigated against Escherichia coli, methicillin-resistant Staphylococcus aureus, and Pseudomonas aeruginosa bacterial strains. It has been interestingly observed that Sn doping enhanced the inhibitory activity of ZnO against S. aureus more efficiently than the other two bacterial strains. From cytotoxicity and reactive oxygen species (ROS) production studies it is found that Sn doping concentration in ZnO does not alter the cytotoxicity and ROS production very much. It has also been observed that undoped and Sn doped ZnO nanostructures are biosafe and biocompatible materials towards SH-SY5Y Cells. The observed behavior of ZnO nanostructures with Sn doping is a new way to prevent bacterial infections of S. aureus, especially on skin, when using these nanostructures in creams or lotions in addition to their sunscreen property as an ultraviolet filter. Structural investigations have confirmed the formation of a single phase wurtzite structure of ZnO. The morphology of ZnO nanostructures is found to vary from spherical to rod shaped as a function of Sn doping. The excitation absorption peak of ZnO is observed to have a blue shift, with Sn doping leading toward a significant tuning in band gap.

On two multigrid algorithms for modeling variational multiphase image segmentation.

In this paper, we present two related multigrid algorithms for multiphase image segmentation. Algorithm I solves the model by Vese-Chan. We first generalize our recently developed multigrid method to this multiphase segmentation model (MG1); we also give a local Fourier analysis for the local smoother which leads to a new and more effective smoother. Although MG1 is found many magnitudes faster than the fast method of additive operator splitting (AOS), both algorithms are not robust with regard to the initial guess. To overcome this dependence on the initial guess, we consider a hierarchical segmentation model which achieves multiphase segmentation by repeated use of the Chan-Vese two-phase model; our Algorithm II solves this model by a multigrid algorithm (MG2). Numerical experiments show that both algorithms are efficient and in particular MG2 is more robust than MG1 with respect to initial guesses. AMS subject classifications: 68U10, 65F10, 65K10.