Staphylococcus aureus is the leading cause of life-threatening infections, frequently originating from unknown or deep-seated foci. Source control and institution of appropriate antibiotics remain challenges, especially with infections due to methicillin-resistant S. aureus (MRSA). In this study, we developed a radiofluorinated analog of para-aminobenzoic acid (2-[18F]F-PABA) and demonstrate that it is an efficient alternative substrate for the S. aureus dihydropteroate synthase (DHPS). 2-[18F]F-PABA rapidly accumulated in vitro within laboratory and clinical (including MRSA) strains of S. aureus but not in mammalian cells. Biodistribution in murine and rat models demonstrated localization at infection sites and rapid renal elimination. In a rat model, 2-[18F]F-PABA positron emission tomography (PET) rapidly differentiated S. aureus infection from sterile inflammation and could also detect therapeutic failures associated with MRSA. These data suggest that 2-[18F]F-PABA has the potential for translation to humans as a rapid, noninvasive diagnostic tool to identify, localize, and monitor S. aureus infections.

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e Information about intralesional pharmacokinetics (PK) and spatial distribution of tuberculosis (TB) drugs is limited and has not been used to optimize dosing recommendations for new or existing drugs. While new techniques can detect drugs and their metabolites within TB granulomas, they are invasive, rely on accurate resection of tissues, and do not capture dynamic drug distribution in the tissues of interest. In this study, we assessed the in situ distribution of 11 C-labeled rifampin in live, Mycobacterium tuberculosis-infected mice that develop necrotic lesions akin to human disease. Dynamic positron emission tomography (PET) imaging was performed over 60 min after injection of [ 11 C]rifampin as a microdose, standardized uptake values (SUV) were calculated, and noncompartmental analysis was used to estimate PK parameters in compartments of interest. [11 C]rifampin was rapidly distributed to all parts of the body and quickly localized to the liver. Areas under the concentration-time curve for the first 60 min (AUC 0 -60 ) in infected and uninfected mice were similar for liver, blood, and brain compartments (P > 0.53) and were uniformly low in brain (10 to 20% of blood values). However, lower concentrations were noted in necrotic lung tissues of infected mice than in healthy lungs (P ‫؍‬ 0.03). Ex vivo two-dimensional matrix-assisted laser desorption ionization (MALDI) imaging confirmed restricted penetration of rifampin into necrotic lung lesions. Noninvasive bioimaging can be used to assess the distribution of drugs into compartments of interest, with potential applications for TB drug regimen development.

BackgroundComputer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.MethodWe review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed.ResultWe have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively.ConclusionWhile CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

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Background Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. Objective To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. Materials and methods 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learningbased classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Results and discussion Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. Conclusions AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening. BACKGROUND Glaucoma and early diagnosis

Recently, small and medium enterprises (SMEs) are increasingly focusing on the implementation of green innovation, mainly due to customers’ increasing environmental consciousness. However, SMEs have not yet achieved any significant accomplishment. The lack of success in implementing green practices is due to various barriers. So, it is crucial to analyze and address these barriers prior to introducing green initiatives. This study prioritizes barriers and solutions to adopt green practices in the context of SMEs in Saudi Arabia. The study develops an integrated decision framework based on symmetry principles to identify main-barriers, sub-barriers, and strategies to overcome these barriers. Six main barriers, 24 sub-barriers, and 10 strategic solutions were identified through literature survey. Then, fuzzy analytical hierarchy process (FAHP) was employed to evaluate main-barriers and sub-barriers. Later, fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) methodology was used to rank strategies. Results of FAHP revealed that the political barrier category holds higher importance than other barriers. Results of FTOPSIS showed that the strategic solution ‘developing research practices to carryout green innovation in SMEs’ is more important in addressing green innovation barriers in SMEs.

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