Klebsiella pneumoniae (K. pneumoniae) is involved in several hospital and community-acquired infections. The prevalence of K. pneumoniae-producing-carbapenemase (KPC) resistance genes rapidly increases and threatens public health worldwide. This study aimed to assess the antibiotic resistance level of K. pneumoniae isolates from Makkah Province, Saudi Arabia, during the Islamic ‘Umrah’ ritual and to identify the plasmid types, presence of genes associated with carbapenem hydrolyzing enzymes, and virulence factors. The phenotypic and genotypic analyses based on the minimum inhibitory concentration (MIC), biofilm formation, PCR, and characterization of KPC-encoding plasmids based on the replicon typing technique (PBRT) were explored. The results showed that most isolates were resistant to carbapenem antibiotics and other antibiotics classes. This study identified sixteen different replicons of plasmids in the isolates and multiple genes encoding carbapenem factors, with bla_VIM and bla_OXA-48 being the most prevalent genes identified in the isolates. However, none of the isolates exhibited positivity for the KPC production activity. In addition, this study also identified six virulence-related genes, including kfu, wabG, uge, rmpA, fimH, and a capsular polysaccharide (CPS). Together, the data reported in this study indicate that the isolated K. pneumoniae during the pilgrimage in Makkah were all resistant to carbapenem antibiotics. Although the isolates lacked KPC production activity, they carried multiple carbapenem-resistant genes and virulence factors, which could drive their resistant phenotype. The need for specialized methods for KPC detection, monitoring the possibility of nosocomial transmission, and diverse therapeutic alternatives are necessary for controlling the spreading of KPC. This study can serve as a reference for clinicians and researchers on types of K. pneumoniae commonly found during religious gathering seasons in Saudi Arabia.

Bacterial classification is a vital step in medical diagnosis. This procedure normally has several stages. An early stage involves inspecting the morphology of the bacterial colonies. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. With advances in image processing, specifically, the use of deep and transfer learning techniques, and the wide availability of cameras, we applied deep and transfer learning techniques to address this task without requiring expert knowledge or sample shipping. We used a convolutional neural network (CNN) to identify different bacterial colonies based on their appearance in images captured by cell phone cameras. In this paper, we collected a dataset that contains images of different bacteria taken by cell phone cameras with various settings. Thus, images of two classes of bacterial colonies were obtained in King Abdulaziz City for Science and Technology. The dataset contains 8,043 images. The experimental results show that our application has high accuracy without requiring expert inspections.

The classification of bacterial colony types is crucial for microbiology and medical diagnostics because it provides essential information on pathogen identification and treatment planning. Traditional classification methods are resource intensive and require specialized equipment and expertise. This study explores the integration of smartphone imaging and transfer learning techniques for bacterial colony classification. We expanded an existing dataset of Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) bacteria grown on MacConkey (MAC) agar by incorporating Pseudomonas aeruginosa (P. aeruginosa) on the same medium, as well as Staphylococcus aureus (S. aureus), P. aeruginosa, K. pneumoniae, and E. coli on brain–heart infusion (BHI) agar. The total numbers of images are 9819 and 12348 in the MAC agar and BHI agar datasets, respectively. Using state-of-the-art convolutional neural networks (CNNs) such as SqueezeNet1.0, MobileNetV3 Small, ShuffleNetV2 X1.5, EfficientNetB0, DenseNet121, and ResNet18, we evaluated classification performance across uncontrolled and controlled imaging environments. We also compared the results for the same bacterial type on different media (MAC agar and BHI agar). The results show that the lightweight models EfficientNetB0 and ShuffleNetV2 X1.5 consistently perform well across datasets, reaching 100% accuracy under controlled conditions. We believe that this work provides a foundation for cost-effective bacterial colony classification on smartphones using transfer learning techniques, ultimately enabling practical implementation. Future work will focus on enhancing model explainability and evaluating deployment feasibility on smartphone devices.