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Background Our work presents the whole genome sequence and phylogenetic analysis of five Leishmania RNA virus 2 (LRV2) isolates obtained from patients with cutaneous leishmaniasis (CL) in Iran. Methods The whole genome sequencing of LRV2 was performed using a primer walking approach. The resulting sequences were analyzed for genetic and haplotype diversity, highlighting their independent evolution and significant genetic divergence. Results The whole genome sequence of the current LRV2 showed high genetic and haplotype diversity. The study also revealed the existence of three distinct clades of LRV2, with the LRV2 sequences infecting L. major , L. aethiopica , and sauroleishmania belonging to separate lineages. These lineages have seemingly evolved independently, as the geographic distribution of their flagellate hosts does not overlap with the Leishmania species. The divergence between these three clades is attributed to considerable antiquity, leading to genetic modifications within the viruses residing in them and resulting in structural differences in their genome. Conclusions These findings contribute to our understanding of the genetic diversity and evolution of LRVs, providing valuable insights into their role in Leishmania infections. Further investigations are needed to understand the significance of these polymorphic sites and their potential impact on viral characteristics and disease outcomes.

MicroRNAs (miRNAs) are a non-coding subclass of endogenous small regulatory RNAs, with about 18–25 nucleotides length which play a critical role in the regulation of gene expression at the post-transcriptional level in eukaryotes. Aberrant expression of miRNAs has the potential to become powerful non-invasive biomarkers in pathological diagnosis and prognosis of different disorders including infectious diseases. Parasite’s life cycle may require the ability to respond to environmental and developmental signals through miRNA-mediated gene expressions. Over the last years, thousands of miRNAs have been identified in the helminthic and protozoan parasites and many pieces of evidence have demonstrated the functional role of miRNAs in the parasites’ life cycle. Detection of these miRNAs in biofluids of infected hosts as prognostic and diagnostic biomarkers in infectious diseases is growing rapidly. In this review, we have highlighted altered expressions of host miRNAs, detected parasitic miRNAs in the infected hosts, and suggested some perspectives for future studies.

Background Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis. Methods In this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model’s interpretability. Results The final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33. Conclusion The newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.

The protozoan Trichomonas vaginalis is a parasite associated with numerous sexually transmitted infections worldwide. In males, although Trichomonas vaginalis infection (also known as trichomoniasis) often remains asymptomatic, it can lead to conditions such as prostatitis or urethritis. Herein, we report the case of a man in his early 90s who was admitted to a hospital in northern Iran with mild dysuria, whose precise onset remained unclear. He had a history of chronic prostate discomfort for several years and acknowledged having multiple sexual partners over the past two decades. He complained of dysuria; therefore, urinalysis was performed, which detected trichomoniasis. He received treatment with metronidazole, resulting in the resolution of urinary symptoms and elimination of the parasite. This case highlights the importance of considering the possibility of trichomoniasis in men, especially older men, as they may face an increased risk of inflammation and prostate cancer.

Introduction Cutaneous Leishmaniasis (CL) is a vector-borne disease caused by a protozoan parasite and considered a public health challenge in many countries, including Iran. Recent research has focused on the role of Leishmania RNA virus (LRV) in the pathogenesis of cutaneous and mucosal forms of leishmaniasis. This study assessed the presence of LRV2 and its genotype diversity among isolates in Ilam province, western Iran, an old focus of Zoonotic CL. Material and method Thirty-two lesion specimens were collected from CL patients and subjected to PCR analysis using species-specific primers for Leishmania identification and detection of LRV2. The amplified products were sequenced, and phylogenetic analysis was performed. Result All samples were identified as L. major . The PCR showed that 4 of 32 (12.5%) clinical cultures were LRV2 positive. Phylogenetic analysis revealed high nucleotide sequence identity (96–99%) with LRV2 isolated from L. major strains from Iran, Turkey, and Uzbekistan. The four sequences analyzed revealed four distinct haplotypes, with high genetic variability among the isolates. Conclusion This study provides the first identification and phylogenetic analysis of LRV2 in CL patients from western Iran and highlights the extensive genetic diversity among LRV2 isolates across different regions, which may have significant implications for disease pathogenesis, transmission patterns, and clinical manifestations.

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID . The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients’ CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

Background Lophomonas blattarum is an emerging protozoan that mostly infects the lower respiratory tract and causes pulmonary lophomoniasis. Radiologic findings in patients with pulmonary lophomoniasis have yet to be studied. Thus, we conducted a registry-based clinical investigation to evaluate the radiologic findings of lophomoniasis. Methods In this cross-sectional study, 34 Lophomonas positive patients were enrolled. Demographic data, relevant characteristics, and radiologic findings of the patients were recorded and analyzed. Results Thirty-four (male = 18, female = 16) patients with an average age of 52.21 ± 20.48 years old were examined. Radiological findings such as Alveolar consolidation (26.5%), Ground glass opacity (5.9%), Centrilobular nodules (23.5%), Tree -in- bud (38.2%), Cavitation (23.5%), Pleural effusion (23.5%), Interstitial opacity (8.8%), Lymphadenopathy (23.5%), Bronchocele (5.9%), Bronchiectasis (29.4%), Nodules (8.8%) and Mass (11.8%) were obtained, that the frequency of all radiological findings was less than 50%. Conclusion In this study, the most common radiological findings in patients with lophomoniasis were tree-in-bud nodules, alveolar consolidation, bronchiectasis, and centrilobular nodules which were mostly seen in the right lung and its middle and lower lobes. Given that the radiologic findings of this disease are unknown, it can be considered in differential diagnosis.

Background Leishmaniasis is a parasitic disease caused by an intracellular protozoan, Leishmania . Various factors, including host immunity and the Leishmania species, influence the manifestation and severity of the disease. Recent investigations have shed light on the potentially significant role of Leishmania RNA virus (LRV) in the clinical prognosis of leishmaniasis. This study aims to investigate the influence of LRV2 + on various pathogenic genes of Leishmania . Materials and methods In this study, 35 Leishmania isolates were obtained from patients diagnosed with cutaneous leishmaniasis (CL). Leishmania species and the presence of LRV2 + were identified with the PCR-RFLP and semi-nested PCR methods, respectively. Additionally, the RNA expression levels of cysteine protease (CP), heat shock protein 70 (HSP70), heat shock protein 83 (HSP83), glycoprotein 63 (GP63), and mannose phosphate isomerase (MPI) were assessed in LRV2 + and LRV2- Leishmania clinical isolates using RT-qPCR. Results Out of the 35 isolates, 20 were selected from CL patients, all confirmed as Leishmania major . These isolates were divided into two groups, LRV2 + and LRV2-, with 10 isolates in each group. RT-qPCR analysis revealed that HSP83, MPI, and GP63 gene expression levels were statistically upregulated in LRV2 + isolates compared to LRV2- isolates ( P  < 0.05). Although HSP70 and CP genes showed slight up-regulation in LRV2 + isolates, it was not statistically significant compared to LRV2- isolates. Conclusion The notable increase in gene expression levels, particularly for GP63 , HSP83 , and MPI genes, suggests that the presence of LRV2 + may significantly influence the expression of these factors in L. major clinical isolates. Clinical trial number Not applicable.

Background Patients with chronic kidney disease (CKD) are susceptible to acquiring opportunistic parasites due to acquired immunodeficiency caused by uremia. Therefore, the present case–control study attempted to determine the prevalence of T. gondii infection and also associated risk factors among patients with CKD under hemodialysis and healthy controls who were registered at the Iranian National Registry Center for Toxoplasmosis (INRCT) in Mazandaran Province, northern Iran. Methods 212 cases with CKD and 200 healthy controls were enrolled in this study. Informed consent as well as a questionnaire were obtained from all subjects. Blood samples were collected from each participant and the serum was screened for anti- Toxoplasma antibodies (IgG and IgM). PCR assay was performed to detect circulating T. gondii in the blood samples of patients and controls using the primer pair targeting the RE gene. Results Out of 412 participants, 67.92% of patients and 15.5% of control subjects were positive for anti- Toxoplasma IgG, but all participants were negative for anti- Toxoplasma IgM. Also, considering PCR assays with RE target, the prevalence of T. gondii infection was 24.1% in case subjects, while none of the control subjects tested positive. Among the PCR positive, 34 (66.7%) had Toxoplasma IgG positivity. The results from the multiple multinomial logistic regression revealed that the seroprevalence of anti- T. gondii IgG antibodies in patients with CKD was 3.12 times higher than in healthy controls (OR = 3.12; 95% CI = 0.43, 14.8; P <  0.001). Also, there was a significant association between seroprevalence of T. gondii infection and age, having a cat at home, and level of glomerular filtration rate (GFR) in these patients. Conclusion Our findings demonstrate a highly significant association between latent T. gondii infection and CKD, mostly in the late stages. Thus, regular screening for T. gondii infection in these patients is strongly recommended to prevent the reactivation of latent infections. A combination of serological screening, chemoprophylaxis, and PCR follow-up for patients at risk of reactivation should effectively reduce the likelihood of latent infection reactivation. Clinical trial number Not applicable.