Surgical correction of aorto-pulmonary window: a rare and lethal cause of pulmonary hypertension

Aorto-pulmonary window is a relatively rare congenital cardiac malformation with an overall incidence of 0.1%. Pulmonary hypertension develops quickly if the lesion is left untreated hence early surgical intervention is warranted after diagnosis. The surgery for correction of APW has evolved over years, currently an open repair on cardiopulmonary bypass (CPB) with a single patch technique yields good results. Mortality is affected by association of pulmonary hypertension and other cardiac malformations. We present a case of an infant with a large type II APW with a relatively low pulmonary vascular resistance. Hospital stay was complicated because of pulmonary arterial disease making it an important reason for correction in the first few months of life

EDeLeaR: Edge-based Deep Learning with Resource Awareness for Efficient Model Training and Inference for IoT and Edge Devices

Deep learning has emerged as a powerful technique for processing and extracting insights from complex data. However, the resource-constrained nature of edge devices poses significant challenges to the deployment of deep learning models at the network edge. This research proposes a novel algorithm called EDeLeaR, which stands for Edge-based Deep Learning with Resource-awareness, to enable efficient model training and inference in edge computing environments. EDeLeaR leverages adaptive resource allocation and optimization techniques to maximize the utilization of limited computational resources while preserving model accuracy and minimizing latency. This paper presents the design, implementation, and evaluation of EDeLeaR, showcasing its effectiveness through comprehensive experiments on real-world edge devices.   &nbsp

PyNanospacing: TEM image processing tool for strain analysis and visualization

The diverse spectrum of material characteristics including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties are intricately intertwined with the atomic structure and the corresponding interatomic bond-lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations, whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. Here we developed a Python code for TEM image processing that can handle a wide range of materials including nanoparticles, 2D materials, pure crystals and solid solutions. This algorithm converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression. The tool is very generic and can significantly aid in analyzing material properties using TEM images, allowing for a more in-depth exploration of the underlying science behind strain engineering via strain contour maps at the atomic level.Comment: Preprint, 13 pages, 9 figure

Prevalence and risk factors for hepatitis C virus infection in an informal settlement in Karachi, Pakistan

The burden of hepatitis C virus (HCV) infection in Pakistan is amongst the highest in the world. People living in slums are likely to be at high risk of infection. Here, we describe the results of a cross-sectional survey conducted in March 2022 that aimed to quantify the prevalence of HCV infection in Machar Colony, one of the largest and oldest slum settlements in Karachi. Risk factors for HCV seropositivity were identified using multi-level logistic regression. We recruited 1,303 individuals in a random selection of 441 households from Machar Colony. The survey-adjusted HCV-seroprevalence was 13.5% (95% Confidence Interval (CI) 11.1–15.8) and survey-adjusted viraemic prevalence was 4.1% (95% CI 3.1–5.4) with a viraemic ratio of 32% (95% CI 24.3–40.5). Of 162 seropositive people, 71 (44%) reported receiving previous treatment for chronic hepatitis C. The odds of HCV seropositivity were found to increase with each additional reported therapeutic injection in the past 12 months (OR = 1.07 (95% Credible Interval (CrI) 1.00–1.13)). We found weaker evidence for a positive association between HCV seropositivity and a reported history of receiving a blood transfusion (OR = 1.72 (95% CrI 0.90–3.21)). The seroprevalence was more than double the previously reported seroprevalence in Sindh Province. The overall proportion of seropositive people that were viraemic was lower than expected. This may reflect the long-term impacts of a non-governmental clinic providing free of cost and easily accessible hepatitis C diagnosis and treatment to the population since 2015. Reuse of needles and syringes is likely to be an important driver of HCV transmission in this setting. Future public health interventions should address the expected risks associated with iatrogenic HCV transmission in this community

Exploring Hydride Formation in Stainless Steel Revisits Theory of Hydrogen Embrittlement

Various mechanisms have been proposed for hydrogen embrittlement, but the causation of hydrogen-induced material degradation has remained unclear. This work shows hydrogen embrittlement due to phase instability (decomposition). In-situ diffraction measurements revealed metastable hydrides formed in stainless steel, typically declared as a non-hydride forming material. Hydride formation is possible by increasing the hydrogen chemical potential during electrochemical charging and low defect formation energy of hydrogen interstitials. Our findings demonstrate that hydrogen-induced material degradation can only be understood if measured in situ and in real-time during the embrittlement process.Comment: 31 Pages, 18 Figures, Preprin