Publications by authors named "D Z Khan"

Pediatric antibiotic labels are common, and unnecessary antibiotic avoidance is associated with negative personal and public health outcomes; as a result, there is an increasing emphasis on the importance of pediatric antibiotic allergy evaluations. Different testing strategies have been advised, including skin testing and challenge testing with varied doses and duration. Established consensus testing protocols are lacking.

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Quinoline is a nitrogen-containing heterocycle compound widely used in the medical industry for its pharmacological properties, such as its antimalarial, antimicrobial, antiparasitic, anti-inflammatory, and anticancer activities. Beyond its medical significance, quinoline shows promising applications in agriculture as a safe and effective pesticide, herbicide, and fertilizer. This review explores the evolution of quinoline research, beginning with its history and synthesis and transitioning to its biological activities and their relevance in agriculture.

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Seed endophytes are actively used by the mother plant as both reservoir and vector of beneficial microbes. During seed dormancy endophytes experience significant physiochemical changes and only competent endophytes could colonise successfully in seeds and some of them act as obligate endophyte that are transmitted vertically across generations. The adaptive nature of endophytes allows them to switch lifestyles depending on environment and host conditions.

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Primary Sjögren syndrome (SS) is an autoimmune disease affecting exocrine glands, with predisposition to development of lymphoma (lymphomagenesis). We report a case of Sjogren's syndrome and discuss the role of FDG PET/CT in the primary diagnosis of lymphoma transformation in SS. Furthermore, we reviewed the literature regarding the utility of FDG PET/CT to assess systemic disease activity and also its role in the SS associated lymphoma with light into the new PET tracers that can be explored for these indications in the future.

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Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.

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