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.

A substantial proportion of coronavirus disease 2019 (COVID-19) survivors continue to suffer from long-COVID-19 (LC) symptoms. Our study aimed to determine the risk factors for LC by using a patient population from Northern Cyprus. Subjects who were diagnosed with severe acute respiratory syndrome-2 (SARS-CoV-2) infection in our university hospital were invited and asked to fill in an online questionnaire. Data from 296 survivors who had recovered from COVID-19 infection at least 28 days prior the study was used in the statistical analysis. For determination of risk factors for “ongoing symptomatic COVID-19 (OSC)” and “Post-COVID-19 (PSC)” syndromes, the patient population was further divided into group 1 (Gr1) and group 2 (Gr2), that included survivors who were diagnosed with COVID-19 within 4-12 weeks and at least three months prior the study, respectively. The number of people with post-vaccination SARS-CoV-2 infection was 266 (89.9%). B.1.617.2 (Delta) (41.9%) was the most common SARS-CoV-2 variant responsible for the infections, followed by BA.1 (Omicron) (34.8%), B.1.1.7 (Alpha) (15.5%), and wild-type SARS-CoV-2 (7.8%). One-hundred-and-nineteen volunteers (40.2%) stated an increased frequency of COVID-19-related symptoms and experienced the symptoms in the week prior to the study. Of those, 81 (38.8%) and 38 (43.7%) were from Gr1 and Gr2 groups, respectively. Female gender, chronic illness, and symptomatic status at PCR testing were identified as risk factors for developing OSC syndrome, while only the latter showed a similar association with PSC symptoms. Our results also suggested that ongoing and persistent COVID-19-related symptoms are not influenced by the initial viral cycle threshold (Ct) values of the SARS-CoV-2, SARS-CoV-2 variant as well as vaccination status and type prior to COVID-19. Therefore, strategies other than vaccination are needed to combat the long-term effect of COVID-19, especially after symptomatic SARS-CoV-2 infection, and their possible economic burden on healthcare settings.

(1) Background: Helminth infections are a widespread global health concern, with Ascaris and taeniasis representing two of the most prevalent infestations. Traditional diagnostic methods, such as egg-based microscopy, are fraught with challenges, including subjectivity and low throughput, often leading to misdiagnosis. This study evaluates the efficacy of advanced deep learning models in accurately classifying Ascaris lumbricoides and Taenia saginata eggs from microscopic images, proposing a technologically enhanced approach for diagnostics in clinical settings. (2) Methods: Three state-of-the-art deep learning models, ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S, are considered. A diverse dataset comprising images of Ascaris, Taenia, and uninfected eggs was utilized for training and validating these models by performing multiclass experiments. (3) Results: All models demonstrated high classificatory accuracy, with ConvNeXt Tiny achieving an F1-score of 98.6%, followed by EfficientNet V2 S at 97.5% and MobileNet V3 S at 98.2% in the experiments. These results prove the potential of deep learning in streamlining and improving the diagnostic process for helminthic infections. The application of deep learning models such as ConvNeXt Tiny, EfficientNet V2 S, and MobileNet V3 S shows promise for efficient and accurate helminth egg classification, potentially significantly enhancing the diagnostic workflow. (4) Conclusion: The study demonstrates the feasibility of leveraging advanced computational techniques in parasitology and points towards a future where rapid, objective, and reliable diagnostics are standard.

Background: Human immunodeficiency virus (HIV) can be transmitted from mothers to their babies during pregnancy, delivery through vaginal fluids or breastfeeding. As false positivity anti-HIV results due to pregnancy could be detected and no relevant study have been reported in Northern Cyprus so far, we aimed to estimate the false anti-HIV positivity rate in pregnant women. Methods: A total of 11.977 women admitted to Near East University Hospital between 2015 and 2018 were involved. The fourth generation anti-HIV-1/2 ELISA test was carried out by chemiluminescence enzyme immunoassay. Positive results were confirmed by real-time polymerase chain reaction (rt-PCR). SPSS (Statistical Package for the Social Sciences) Demo Ver 22 program was used for statistical analysis and significance (p<0.05) was measured by Person Chi-Square and Fisher's Exact tests. Results: Anti-HIV-1/2 ELISA test was positive in 7 (0.3%) of pregnant and 11 (0.1%) of non-pregnant women. HIV RNA was not detected in any pregnant however, was detected in 2 (0.02%) of non pregnant. S/Co titer of pregnant and non pregnant who have positive anti-HIV-test without viral load was x̄=2.68±1.64 (1.34-5.20) and x̄=8.63±7.68 (1.56-20.98) respectively. False positivity was significantly higher in pregnants compared to non-pregnants (p=0.033). Conclusion: False positivity can be encountered during pregnancy therefore, positive anti-HIV-1/2 ELISA results should be confirmed with molecular techniques before initiating antiretroviral treatment.

Objective: The aim of this study was to determine the antioxidant and antimicrobial activity of Ficus sycomorus fruit and leaf extracts obtained from the Turkish Republic of Northern Cyprus. Materials and Methods: Fruits and leaves of F. sycomorus were collected from the Kyrenia region of Northern Cyprus in July 2018. The leaf and fruit samples were extracted with the distilled water, methanol, ethanol, acetone and chloroform solvents (1:10 [w/v]). After evaporation, samples were suspended in methanol at the final concentration of 100 mg/mL. The antimicrobial activity of the leaf and fruit extracts was evaluated using the Kirby-Bauer Disk Diffusion Method. Total phenolic content (TPC) and total flavonoid content (TFC) of the extracts were determined using the methods reported by Stankovic in 2011 and Sharm and Vig in 2013. Antioxidant activity of samples was tested using free radical scavenger method. Results: The leaf extracts of F. sycomorus was active. The inhibition zone diameters ranged from 1.8mm to 13.00 mm. Fruit extracts and methanol controls showed no inhibitory effect on strains. However, bacteriostatic activity against Enterococcus faecalis was observed in fruit-water extract. The highest antimicrobial activity was shown against Staphylococcus aureus (13 mm) for ethanolic extracts at 100 mg/mL concentration. Minimal inhibition concentration (MIC) for ethanolic extract was observed starting at 25 mg/mL concentration against S. aureus. Although, no antimicrobial activiy was observed in fruit extracts, the highest 2,2-diphenyl 1-picrylhydrazyl (DPPH) activiy and phytochemical content were recorded in fruit extracts. Conclusion: These results demonstrate that leaf extracts of F. sycomorus can be used as a curative agent for the treatment of S. aureus and fungal infections and may be effective against pathogenic microorganisms that are resistant to antibiotics. Antioxidant content of fruit and leaf extracts can be effective against the negative effects of free radicals.