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Undernutrition among under-fives is one of the major public health challenges in Tanzania. However, there are limited studies assessing the contribution of cultural-related strategies in the prevention of child undernutrition in Tanzania. This study aimed at exploring participants' experiential views regarding developing culturally sensitive strategies for the elimination of child undernutrition for under-fives in Rukwa, Iringa, Ruvuma, Songwe and Njombe regions located in the Southern Highlands in Tanzania. This study applied focus group discussions (FGDs) with forty practitioners to explore culturally-sensitive strategies for effectively preventing child undernutrition in Tanzania. The study participants were purposively selected, and thematic analysis was used to identify themes within the data. This study revealed that district- and lower-level administrative systems should prioritize nutrition interventions in their plans, allocating adequate resources to implement culturally sensitive nutrition interventions, while national-level organs need to strengthen institutional capacity and ensure the availability of funds, skilled human resources and a legal framework for the effective implementation and sustainability of nutrition interventions at the district- and lower-levels. This study highlights that for the successful implementation of culturally sensitive strategies towards the elimination of child undernutrition, there is a need to use a systems approach that allows for collaborative governance whereby different sectors act together to address the persistent malnutrition epidemic. [ABSTRACT FROM AUTHOR]

Multisource cone-beam computed tomography (CT) is an attractive approach of choice for superior temporal resolution, which is critically important for cardiac imaging and contrast enhanced studies. In this paper, we present a filtered-backprojection (FBP) algorithm for triple-source helical cone-beam CT. The algorithm is both exact and efficient. It utilizes data from three inter-helix PI-arcs associated with the inter-helix PI-lines and the minimum detection windows defined for the triple-source configuration. The proof of the formula is based on the geometric relations specific to triple-source helical cone-beam scanning. Simulation results demonstrate the validity of the reconstruction algorithm. This algorithm is also extended to a multisource version for (2N + 1)-source helical cone-beam CT. With parallel computing, the proposed FBP algorithms can be significantly faster than our previously published multisource backprojection-filtration algorithms. Thus, the FBP algorithms are promising in applications of triple-source helical cone-beam CT. Index Terms--Computed tomography (CT), cone-beam, filtered-backprojection (FBP), helical scanning, triple-source.

Supplier development and supplier integration are two deeply interconnected strategic tools that manufacturers often employ to improve the efficiency and competitiveness of their supply chains. This paper studies the interaction of these two strategic decisions in a competitive environment. Specifically, we consider a duopoly model where two supply chains, each consisting of one manufacturer and one supplier, sell substitutable products in the market. Each manufacturer orders components from its supplier and decides whether to integrate with the supplier and how much to invest to help reduce its cost. We find that, in most cases, a manufacturer invests more on supplier development after it integrates with its supplier; and both manufacturers integrate with their suppliers at equilibrium. However, when the heterogeneity of the manufacturers' supplier development capabilities is sufficiently high and the more capable manufacturer integrates, the less capable manufacturer would invest less on supplier development after integrating its supplier; and at equilibrium, the less capable manufacturer prefers not to integrate. Furthermore, when considering the suppliers' incentives to be integrated, we find that the less capable manufacturer is more likely not to integrate with its supplier. We also extend our model to another commonly seen sourcing structure, that is, diversified sourcing, where each manufacturer sources from two common suppliers (but each only considers supplier integration and development for one distinct supplier). In contrast to the previous case (which we call dedicated sourcing), under diversified sourcing, supplier integration of a manufacturer always leads to its more supplier development investment and both manufacturers would integrate at equilibrium. Finally, when each manufacturer could choose whether to source from one or two suppliers, we show that dedicated sourcing is the equilibrium structure. [ABSTRACT FROM AUTHOR]

Objective: Under nutrition especially among under-fives is a major public health challenge in Tanzania. However, the contribution of cultural practices to child under nutrition is often overlooked. This study aimed to explore the perceived socio-cultural factors contributing to the persisting under nutrition among under-fives in Tanzania. Methods: The study applied focus group discussion (FDGs) with forty practitioners to examine the sociocultural factors contributing to under nutrition during early childhood. The study participants were purposively selected and thematic analysis was used to identify themes within the data. Results: This study revealed that, under nutrition for under-fives is caused by a number of socio-cultural factors including existence of gender inequality related to dietary practices and qualities, women's excessive workload, patriarchy social-norm, excessive alcohol use and cultural taboos prohibiting women and girls from eating certain types of nutrient dense foods. Conclusion: The study highlights the multiplicity of factors including socio-cultural perspectives contributing to under nutrition among under-fives, and calls for a concerted efforts in developing and implementing issue-specific and culturally sensitive strategies towards elimination of child under nutrition., Competing Interests: The authors declare that they do not have any conflicts of interest., (Copyright © 2023 Frumence, Jin, Kasangala, Mang’enya, Bakar and Ochieng.)

Diabetes mellitus is a chronic metabolic disorder caused either by inadequate insulin secretion, impaired insulin function, or both. Uncontrolled diabetes is characterized by hyperglycemia which over time leads to fatal damage to both macro-and microvascular systems, causing complications such as cardiovascular diseases, retinopathy and nephropathy. Diabetes management is conventionally delivered through modifications of diet and lifestyle and pharmacological treatment, using antidiabetic drugs, and ultimately insulin injections. However, the side effects and financial cost of medications often reduce patient compliance to treatment, negatively affecting their health outcomes. Natural phytochemicals from edible plants such as fruits and vegetables (F&V) and medicinal herbs have drawn a growing interest as potential therapeutic agents for treating diabetes and preventing the onset and progression of diabetic complications. Flavonoids, the most abundant polyphenols in the human diet, have shown antidiabetic effects in numerous in vitro and preclinical studies. The underlying mechanisms have been linked to their antioxidant, anti-inflammatory and immunomodulatory activities. Carotenoids, another major group of dietary phytochemicals, have also shown antidiabetic potential in recent in vitro and in vivo experimental models, possibly through a mechanism of action similar to that of flavonoids. However, scientific evidence on the efficacy of these phytochemicals in treating diabetes or preventing the onset and progression of its complications in clinical settings is scarce, which delays the translation of animal study evidence to human applications and also limits the knowledge on their modes of actions in diabetes management. This review is aimed to highlight the potential roles of flavonoids and carotenoids in preventing or ameliorating diabetes-related complications based on in vivo study evidence, i.e., an array of preclinical animal studies and human intervention trials. The current general consensus of the underlying mechanisms of action exerted by both groups of phytochemicals is that their anti-inflammatory action is key. However, other potential mechanisms of action are considered. In total, 50 in vivo studies were selected for a review after a comprehensive database search via PubMed and ScienceDirect from January 2002 to August 2022. The key words used for analysis are type-2 diabetes (T2DM), diabetic complications, flavonoids, carotenoids, antioxidant, anti-inflammatory, mechanisms of prevention and amelioration, animal studies and human interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jin and Arroo.)

Machine learning and deep learning are rapidly finding applications in the medical imaging field. In this paper, we address the long-standing problem of metal artifacts in computed tomography (CT) images by training a dual-stream deep convolutional neural network for streak removal. While many metal artifact reduction methods exist, even state-of-the-art algorithms fall short in some clinical applications. Specifically, proton therapy planning requires high image quality with accurate tumor volumes to ensure treatment success. We explore a dual-stream deep network structure with residual learning to correct metal streak artifacts after a first-pass by a state-of-the-art interpolation-based algorithm, NMAR. We provide the network with a mask of the streaks in order to focus attention on those areas. Our experiments compare a mean squared error loss function with a perceptual loss function to emphasize preservation of image features and texture. Both visual and quantitative metrics are used to assess the resulting image quality for metal implant cases. Success may be due to the duality of information processing, with one network stream performing local structure correction, while the other stream provides an attention mechanism to destreak effectively. This study shows that image-domain deep learning can be highly effective for metal artifact reduction (MAR), and highlights the benefits and drawbacks of different loss functions for solving a major CT reconstruction challenge.

A significant and increasing number of patients receiving radiation therapy present with metal objects close to, or even within, the treatment area, resulting in artifacts in computed tomography (CT) imaging, which is the most commonly used imaging method for treatment planning in radiation therapy. In the presence of metal implants, such as dental fillings in treatment of head-and-neck tumors, spinal stabilization implants in spinal or paraspinal treatment or hip replacements in prostate cancer treatments, the extreme photon absorption by the metal object leads to prominent image artifacts. Although current CT scanners include a series of correction steps for beam hardening, scattered radiation and noisy measurements, when metal implants exist within or close to the treatment area, these corrections do not suffice. CT metal artifacts affect negatively the treatment planning of radiation therapy either by causing difficulties to delineate the target volume or by reducing the dose calculation accuracy. Various metal artifact reduction (MAR) methods have been explored in terms of improvement of organ delineation and dose calculation in radiation therapy treatment planning, depending on the type of radiation treatment and location of the metal implant and treatment site. Including a brief description of the available CT MAR methods that have been applied in radiation therapy, this article attempts to provide a comprehensive review on the dosimetric effect of the presence of CT metal artifacts in treatment planning, as reported in the literature, and the potential improvement suggested by different MAR approaches. The impact of artifacts on the treatment planning and delivery accuracy is discussed in the context of different modalities, such as photon external beam, brachytherapy and particle therapy, as well as by type and location of metal implants.

Computed tomography (CT) has been used for a variety of applications, two of which include diagnostic imaging and attenuation correction for PET or SPECT imaging. Ideally, the x-ray tube spectrum should be optimized for the specific application to minimize the patient radiation dose while still providing the necessary information. In this study, we proposed a projection-based analytic approach for the analysis of contrast, noise, and bias. Dose normalized contrast to noise ratio (CNRD), inverse noise normalized by dose (IND) and bias are used as evaluation metrics to determine the optimal x-ray spectrum. Our simulation investigated the dose efficiency of the x-ray spectrum ranging from 40 kVp to 200 kVp. Water cylinders with diameters of 15 cm, 24 cm, and 35 cm were used in the simulation to cover a variety of patient sizes. The effects of electronic noise and pre-patient copper filtration were also evaluated. A customized 24 cm CTDI-like phantom with 13 mm diameter inserts filled with iodine (10 mg ml -1 ), tantalum (10 mg ml -1 ), water, and PMMA was measured with both standard (1.5 mGy) and ultra-low (0.2 mGy) dose to verify the simulation results at tube voltages of 80, 100, 120, and 140 kVp. For contrast-enhanced diagnostic imaging, the simulation results indicated that for high dose without filtration, the optimal kVp for water contrast is approximately 100 kVp for a 15 cm water cylinder. However, the 60 kVp spectrum produces the highest CNRD for bone and iodine. The optimal kVp for tantalum has two selections: approximately 50 and 100 kVp. The kVp that maximizes CNRD increases when the object size increases. The trend in the CTDI phantom measurements agrees with the simulation results, which also agrees with previous studies. Copper filtration improved the dose efficiency for water and tantalum, but reduced the iodine and bone dose efficiency in a clinically-relevant range (70-140 kVp). Our study also shows that for CT-based attenuation correction applications for PET or SPECT, a higher-kVp spectrum with copper filtration is preferable. This method is developed based on filter back projection and does not require image reconstruction or Monte Carlo dose estimates; thus, it could potentially be used for patient-specific and task-based on-the-fly protocol optimization.

In X-ray computed tomography (CT), the presence of metallic parts in patients causes serious artifacts and degrades image quality. Many algorithms were published for metal artifact reduction (MAR) over the past decades with various degrees of success but without a perfect solution. Some MAR algorithms are based on the assumption that metal artifacts are due only to strong beam hardening and may fail in the case of serious photon starvation. Iterative methods handle photon starvation by discarding or underweighting corrupted data, but the results are not always stable and they come with high computational cost. In this paper, we propose a high-kVp-assisted CT scan mode combining a standard CT scan with a few projection views at a high-kVp value to obtain critical projection information near the metal parts. This method only requires minor hardware modifications on a modern CT scanner. Two MAR algorithms are proposed: dual-energy normalized MAR (DNMAR) and high-energy embedded MAR (HEMAR), aiming at situations without and with photon starvation respectively. Simulation results obtained with the CT simulator CatSim demonstrate that the proposed DNMAR and HEMAR methods can eliminate metal artifacts effectively.

Background: We are interested in exploring dedicated, high-performance cardiac CT systems optimized to provide the best tradeoff between system cost, image quality, and radiation dose., Objective: We sought to identify and evaluate a broad range of CT architectures that could provide an optimal, dedicated cardiac CT solution., Methods: We identified and evaluated thirty candidate architectures using consistent design choices. We defined specific evaluation metrics related to cost and performance. We then scored the candidates versus the defined metrics. Lastly, we applied a weighting system to combine scores for all metrics into a single overall score for each architecture. CT experts with backgrounds in cardiovascular radiology, x-ray physics, CT hardware and CT algorithms performed the scoring and weighting., Results: We found nearly a twofold difference between the most and the least promising candidate architectures. Architectures employed by contemporary commercial diagnostic CT systems were among the highest-scoring candidates. We identified six architectures that show sufficient promise to merit further in-depth analysis and comparison., Conclusion: Our results suggest that contemporary diagnostic CT system architectures outperform most other candidates that we evaluated, but the results for a few alternatives were relatively close. We selected six representative high-scoring candidates for more detailed design and further comparative evaluation.

Computed Tomography (CT) has been in clinical use for several decades. The number of CT scans has increased significantly worldwide, which results in increased radiation dose delivered to the general population. Many technologies have been developed to minimize the dose from CT scans, including scanner hardware improvements, task-specific protocol design and advanced reconstruction algorithms. In this study, we focused on selection of X-ray tube voltage and filtration to achieve optimal dose efficiency given required image quality, more specifically the contrast to noise ratio. Our approach differs from previous studies in two aspects. Typically, Monte-Carlo simulation is used to estimate dose in simulations, but this is computationally costly. We instead use a projection-domain dose estimation method. No image reconstruction is required for the projection-domain method, which further simplifies the analysis. This study also includes tantalum, a new contrast agent, in addition to soft tissue (water), bone and iodine contrast. Optimal tube voltages and filtration are identified as a function of phantom size. The simulation analysis is confirmed with a limited phantom study.