International Journal of Medical Laboratory

187-195

Authors: Mehdi Taheri Sarvtin , Mohadeseh Kamali *

Psoriasis is an immune-mediated disease causing raised, scaly patches on the skin, especially the elbows, knees, and scalp due to systemic inflammation. The disease affects more than 125 million people worldwide. Psoriasis is associated with problems such as depression, reduced quality of life, cardiovascular diseases, stroke, lymphoma, diabetes mellitus, and metabolic syndrome. Psoriasis has a complex pathogenesis that is not yet fully understood. Many studies have shown the importance of the microbiome for the exacerbation of psoriasis. On the other hand, psoriasis and some other treatments can increase the colonization of some microbial agents in the body. Candida is commensal yeast that forms part of the natural microflora of the skin and mucous membranes. It has been shown that people with psoriasis are susceptible to candidiasis. In this review article, the causes of candidiasis, its diagnosis, and treatment in patients with psoriasis, as well as psoriasis aggravation methods by Candida species have also been investigated. To this end, keywords such as "psoriasis", "candida", " oral candidiasis", "cutaneous candidiasis", "vulvovaginal candidiasis", "epidemiology of Candida" and "balanitis" were searched. Articles published in national and international scientific databases, namely Google Scholar, PubMed/MEDLINE, Elsevier databases, Scopus, Science Direct, IranMedex, and SID from 2001 to 2023 were used. Oral candidiasis, cutaneous candidiasis, vulvovaginal candidiasis, and balanitis are diseases that affect people with psoriasis. On the other. This fungus may aggravate psoriasis through toxin production and activation of T lymphocytes independent of antigen presentation.

208-216

Authors: Mohammad Panji , Vahideh Behmard , Mehri Fallah Raoufi , Elnaz Sadat Mousavi Sadr Jadidi , Elham Rostami , Rahil Norbakhsh , Vajiheh Najafi , Tohid Moradi Gardeshi , Fahimeh Safizadeh , Haleh Barmaki *

Background and Aims: Cerebrospinal fluid (CSF) and blood biomarkers are widely evaluated as diagnostic tools to distinguish between bacterial meningitis and viral meningitis in emergency laboratory tests. So, this aimed to examine the levels of diagnostic parameters in blood and CSF to discriminate between bacterial and viral meningitis in young children referred to Khoy Hospital. Materials and Methods: A total of 101 young children with meningitis were enrolled in this prospective study. The diagnosis of bacterial and viral meningitis was based on clinical features and laboratory findings. Results: Of the 101 patients with meningitis, 18 (17.82%) were bacterial, and 83 (82.17%) were viral meningitis. The levels of CSF glucose and CSF/blood glucose ratio were significantly lower in the bacterial meningitis group than in the viral meningitis group (p < 0.01). In contrast, white blood cells count, CSF protein, interleukin-6, C-reactive protein, and ferritin levels were significantly higher in patients with bacterial meningitis compared to viral meningitis (p < 0.01). However, there was no difference between viral and bacterial meningitis groups concerning mean serum glucose. Conclusion: This study suggests that decreased CSF glucose and CSF/blood glucose ratio and increased white blood cells count, CSF protein, ferritin, interleukin-6, and C-reactive protein combined with clinical symptoms can help better diagnosis of bacterial meningitis, especially in comparison with viral meningitis.

229-237

Authors: Zahra Safaie , Saied Ghorbani , Zahra Salavatiha , Ahmad Tavakoli , Seyed Jalal Kiani , Seyed Hamidreza Monavari *

Background and Aims: Human herpes virus types 6 and 7 (HHV-6, HHV-7) can remain latent in the urogenital tract epithelial cells. Varicella-Zoster virus (VZV) is among the Alphaherpesviruses infecting most people during childhood. This virus can remain latent in the ganglionic neurons. The latent infection can reactivate through weakness, suppression of the immune system, and aging. Therefore, this study investigated the molecular prevalence of HHV-6, HHV-7, and Varicella-Zoster virus in sperm samples from infertile men. Materials and Methods: Sperm samples from 82 infertile patients were collected from infertility centers in Tehran. Chromatin health levels and sperm DNA examinations were carried out using sperm DNA fragmentation index kits. Viral nucleic acids were extracted from the samples. The presence of VZV, HHV-6, and HHV-7 genomes was examined through the conventional polymerase chain reaction assay. Results: The average age was 37.3 ± 6.1 years. The mean sperm motility percentage was 33.6 ± 2%. Infertile men's mean DNA fragmentation index was 27.2 ± 1.2. This study shows a 7.3% and 4.9% prevalence for HHV-6 and VZV, respectively, and the absence of HHV-7 in sperm specimens. Conclusion: Although the infection rates of these herpesviruses seem relatively low, more comprehensive and multi-center studies are required to obtain more accurate results.

247-253

Authors: Ali Gholami , Parastoo Yousefi , Alireza Tabibzadeh *

Background and Aims: The Coronavirus disease 2019 (COVID-19) pandemic began in 2020. A major problem during COVID-19 was determining the clinical severity. There are a variety of markers for assessing the COVID-19 severity and outcome. So, this study aims to introduce a new approach for determining the disease severity based on the laboratory data obtained by machine learning algorithms. Materials and Methods: In this study, we used 100 patients for modeling. We used demographical, background disease, and laboratory data of COVID-19 patients as parameters for training the convolutional neural network model to evaluate disease severity and tried to create a predictive algorithm for future data. The sequential neural network from the Keras library by TensorFlow was used for prediction. The clinical validation of prediction by model was evaluated by the receiver operating characteristic (ROC) curve. Results: The mean F1 score for our current model was 0.62 (in the range of 0-1). The F1 scores for the severe group and the mild group were 0.8 and 0.45, respectively. The ROC curve for clinical validity revealed an acceptable Area Under Curve (0.085) for both severe and mild categories. Conclusion: The current study introduces a simple machine learning algorithm as tool for determining COVID-19 severity of by acceptable ROC. This study can lead us to use such algorithms more often in laboratory medicine and clinical decision-making. Furthermore, the present study is just a preliminary study and highlights the need for further research to validate and refine the proposed model.