Antibiotic-resistant Enterobacteriaceae in the gastrointestinal flora can lead to infections with limited therapeutic options. Also, the resistant bacteria can be transferred from colonized persons to others. The present study was conducted to search the fecal carriage rates of (i) Enterobacteriaceae that produce extended-spectrum β-lactamase (ESBL-E) and/or (ii) plasmid-mediated AmpC β-lactamase (pAmpC-E), (iii) ciprofloxacin-resistant Enterobacteriaceae (CIP-RE), and (iv) carbapenem-intermediate or -resistant Enterobacteriaceae (CIRE) in Northern Cyprus. A total of 500 community-dwellers were recruited from consecutive admissions to the clinical laboratories of four hospitals. One rectal swab or stool sample was collected from each participant. A questionnaire was applied to evaluate possible risk factors associated with intestinal colonization of resistant bacteria. The samples were cultured on antibiotic containing media to screen for resistant bacteria colonization. The bacterial colonies that grew on the plates were subjected to further phenotypic tests to confirm the resistance. Of 500 volunteers, ESBL-E, pAmpC-E, CIP-RE and CIRE carriage were detected in 107 (21.4%), 15 (3.0%), 51 (10.2%) and six (1.2%) participants, respectively. Escherichia coli was the most commonly recovered species among Enterobacteriaceae isolates. A significant proportion of ESBL-producing E. coli isolates (n = 22/107; 20.6%) was found to be co-resistant to CIP (p = 0.000, OR 3.21, 95% CI 1.76–5.87). In this study, higher socioeconomic status (CIP-RE: p = 0.024, OR 1.96, 95% CI 1.09–3.53), presence of gastrointestinal symptoms (CIRE: p = 0.033; OR 6.79, 95% CI 1.34–34.39), antibiotic use (ESBL-E: p = 0.031; OR 1.67, 95% CI 1.04–2.67; and CIRE: p = 0.033; OR 6.40, 95% CI 1.16–35.39), and travelling abroad (pAmpC-E: p = 0.010; OR 4.12, 95% CI 1.45–11.66) were indentified as risk factors. The study indicates that resistant Enterobacteriaceae isolates are carried by humans in the community. To prevent further spread of resistance, rational use of antibiotics should be encouraged, and antibiotic resistance should be carefully monitored in Northern Cyprus.

Malaria is a blood disease caused by the Plasmodium parasite that is transmitted through the bite of female Anopheles mosquitoes. These mosquitoes can cross borders without passports or visas, making malaria a global health concern. To effectively treat malaria, infectious disease specialists must monitor the efficacy of the treatment by counting the number of parasites in a patient’s blood at various time intervals. However, this task is challenging because it involves examining thin or thick blood smear samples under a microscope, which can be tiring to the human eye, particularly when there are many infected patients or when there is a shortage of clinical experts. In such cases, rapid diagnosis is crucial. One approach is to capture microscopic images of blood smear samples using a camera and then employ deep learning-based object detection models to detect and count the infected red blood cells. In this study, state-of-the-art object detection models, including CenterNet, EfficientDet, Faster R-CNN, RetinaNet, and YOLOv8, were explored. The dataset was generated using thin blood smear images in the laboratory. The results revealed that YOLOv8s outperformed the other models, achieving an score of 0.9031 and an mAP@[0.50:0.05:0.95] score of 0.5957. This study also found that various model combinations and ensemble strategies could improve the detection of malaria parasites. Specifically, the weighted boxes fusion ensembling approach achieved an score of 0.9186 and an mAP@[0.50:0.05:0.95] score of 0.6196. In contrast, the non-maximum weighted method achieved an score of 0.9324 and an mAP@[0.50:0.05:0.95] score of 0.6214.

Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa’s northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models’ effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models’ outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew’s correlation coefficient, and Cohen’s Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.

Background: Widespread prevalence of extended-spectrum βeta-lactamase producing Escherichia coli (ESBL-producing E. coli) limits the infection therapeutic options and is a growing global health problem. In this study our aim was to investigate the antimicrobial resistance profile of the E. coli in hospitalized and out- patients in Cyprus. Results: During the period 2010-2014, 389 strains of E. coli were isolated from urine samples of hospitalized and out-patients in Cyprus. ESBL-producing E. coli, was observed in 53% of hospitalized and 44% in out-patients, latest one being in 2014. All ESBL-producing E. coli remained susceptible to amikacin, carbapenems except ertapenem (in-patients= 6%, out-patients= 11%). Conclusions: High emerging ESBL-producing E. coli from urine samples in hospitalized and out-patients is an extremely worrisome sign of development of untreatable infections in the near future on the island. We therefore emphasize the immediate need for establishment of optimal therapy guidelines based on the country specific surveillance programs. The need for urgent prescription habit changes and ban of over-the-counter sale of antimicrobials at each segment of healthcare services is also discussed in this research.

Detection of borderline and/or low positive anti-HCV results by enzyme immunoassay (EIA) leads to severe problems in routine laboratories and needs confirmation with nucleic acid amplification tests which can increase the cost. In EIA tests, if the ratio of sample to cut-off (S/Co) is ≥ 1, the sample is accepted as positive according to the manufacturers' instructions. Although over the last decade the application of S/Co values have also applied to HCV-RNA readings, the current study aims to determine whether the S/Co value is adequate and applicable for the anti-HCV EIA test, and to determine whether a correlation exists between HCV-RNA and HCV infections. A total of 658 cases (402 female, 256 male; mean age: 49.4 ± 17.0 years) who were found anti-HCV positive between January 2011-July 2013 were included in the study. Anti-HCV tests were performed by chemiluminescent EIA (Architect i2000SR, Abbott, USA and LiaisonXL Murex, DiaSorin, Italy) and HCV-RNA by real-time PCR (Cobas Ampliprep/Cobas TaqMan HCV, Roche, USA). The mean S/Co value of the cases was 7.3 ± 4.8 (range: 1.00-17.59) and mean HCV-RNA value was 2.3x105 ± 2.1x106 copies/ml. When the anti-HCV S/Co value of varying ranges was compared with HCV-RNA readings a particular trend was noted. In the anti-HCV S/Co values of 1.0-4.0; 4.1-7.0; 7.1-10.0; 10.1-13.0; 13.1-16.0 and ³16.1, HCV-RNA positivity rates were detected as 1.9%, 24.7%,37.1%, 46.7%, 56.4% and 75%, respectively. Statistical analysis indicated an intermediate positive correlation (r= 0.454) between anti-HCV ve HCV-RNA readings (p= 0.000). An adequate S/Co value was accepted as 5.0 based on the ROC analysis, and this value gave a performance confidence level of 95.6% when determining whether a patient is HCV positive. Based on the data of this study it became evident that further EIA testing is not required if the S/Co value is ≥ 5.0, however if the S/Co value is less than 5.0, then further clinical analysis and revaluation of the patient is required.