International Journal of Medical Laboratory

Background and Aims: Human cytomegalovirus (HCMV) can induce latency and evade the immune system. The latent virus can reactive later in older ages and due to immunosuppressive conditions. Sexually transmitted diseases and viruses can influence the male reproductive system, and members of the Herpesviridae family are one of these important viruses. Regarding the importance of herpesviruses, specially HCMV, this research aimed to evaluate the prevalence of HCMV in semen samples of infertile men.
Materials and Methods: Semen samples were collected from infertility centers affiliated to Iran University of Medical Sciences. The chromatin state was assessed by DNA fragmentation index. Nucleic acids were extracted from the semen specimens, and a real-time polymerase chain reaction assay was performed to detect HCMV DNA.
Results: Enrolled patients were 82 infertile men. The mean age of participants was 37.3 ± 6.1 years, and the mean motility and DNA fragmentation index of the samples were 33.6 ± 2 % and 27.2 ± 1.2, respectively. The prevalence of HCMV was 11%, and there was no statistically significant difference between HCMV and sperm parameters except the motility.
Conclusions: HCMV infection could be important in sperm motility, and HCMV prevalence in infertile patients was 11%. Further investigations in this field can be helpful for a clear result in the future.

Background and Aims: Retinoblastoma tumors are the most common intraocular malignancy in childhood, leading to death after two years. The Human Adenovirus (HAdV) infection could be critical in the retinoblastoma pathogenesis due to the virus and retinoblastoma 1 interactions. The objective of the current study was to investigate the possible presence of the HAdV genome in the retinoblastoma patient's tumors.
Materials and Methods: In this study, we evaluated the HAdV infection in 96 pathological confirmed retinoblastoma samples. The DNA was extracted from formalin-fixed paraffin-embedded blocks, and the virus infection was assessed using polymerase chain reaction. SPSS version 22 was used for statistical analysis.
Results: The mean age ± SD of the retinoblastoma patients was 28.89 ± 17 months. In addition, the demographic evaluation indicated that 43 (46.7%) of patients were female. The retinoblastoma laterality assessment indicates 87 (90.4%) unilateral and 9 (9.4%) bilateral tumors. Growth pattern analysis indicates endophytic 58 (77.3%), exophytic 8 (10.7%), and 9 (12%) of tumors with mix endophytic and exophytic patterns. The polymerase chain reaction results could not found any evidence of HAdV infection in all 96 formalin-fixed paraffin-embedded samples.
Conclusions: The study results suggest that there is not any association between HAdV infection and retinoblastoma tumors in studied samples. The HAdV infection may not a concern in retinoblastoma pathogenesis. Further investigations are recommended in this field of study.

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.