Many patients with diabetes develop foot ulcers that are very susceptible to infections which may place the patients at risk for lower-limb amputation. Such infections require adequate management with antibiotics. The authors performed a systematic analysis and critical review of studies with the dual aim of assessing bacterial profiles and antimicrobial susceptibility patterns in patients with diabetic foot infections using various methods for sample collection and evaluating the safety and efficacy of ertapenem as initial empirical treatment for such infections. Following a selection of only studies with adequate description of methods for pathogen isolation and antibiogram determination, nine studies were included. The need for adequate prospective multicenter studies to assess the value of empirical antibiotic regimens for diabetic foot became evident and the conclusions were as follows: proper identification of causative agents, appropriate antibiotic therapy and management of complications in these infections are essential to achieve a successful outcome; the sampling procedure is extremely important in the evaluation of the microbial flora of the foot ulcer; susceptibility testing should be performed routinely at least for those species with unpredictable resistance; and ertapenem has been shown to be useful in the treatment of a wide range of moderate-to-severe lower extremity infections in a broad spectrum of patients.
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http://dx.doi.org/10.1358/dot.2013.49(Suppl.A).1981340 | DOI Listing |
BMC Endocr Disord
January 2025
Burn and Wound Repair Department, Fujian Medical University Union Hospital, Fuzhou, China.
Background: Diabetic foot ulcers (DFUs) are characterized by dynamic wound microbiome, the timely and accurate identification of pathogens in the clinic is required to initiate precise and individualized treatment. Metagenomic next-generation sequencing (mNGS) has been a useful supplement to routine culture method for the etiological diagnosis of DFUs. In this study, we utilized a routine culture method and mNGS to analyze the same DFU wound samples and the results were compared.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Computer Science Department, University of Geneva, Geneva, Switzerland.
Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard (Deemed University), M. B. Road, New Delhi 110062, India. Electronic address:
Infect Dis Rep
January 2025
Diabetic Foot Unit, Clínica Universitaria de Podología, Facultad de Enfermería, Fisioterapia y Podología, Universidad Complutense de Madrid, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain.
This systematic review reports on treatments for onychomycosis in patients with diabetes and the drug interactions with other drugs in regard to the complicated diabetic patient profile. The recommendations in the preferred reporting items for systematic reviews and meta-analysis (PRISMA) checklist were applied and the included studies were evaluated using the Consolidated Standards of Reporting Trials (CONSORT) statement and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Searches were conducted in November 2023, using the PubMed (Medline), Scopus, Cochrane Library, and Web of Science databases; studies on antifungal treatments for onychomycosis in patients with diabetes were included.
View Article and Find Full Text PDFJ Pain Res
January 2025
Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, 530021, People's Republic of China.
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