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Background: Toxoplasmosis is a serious or life-threatening disease in immunosuppressed patients and pregnant women. This study examined the likely association between Toxoplasma gondii infection and COVID-19 patients with moderate illness. Methods: Seventy blood samples were collected from patients at the Health Reference Laboratory of Tabriz, Northwest Iran from April 2021 to September 2021. In addition, 70 healthy subjects of the same age (37 ± 15 years) and sex distribution were ethnically matched. Sera samples were examined for the detection of anti-Toxoplasma antibodies using ELISA. Nested-PCR targets were amplified based on the B1 and GRA6 genes. GRA6 amplicons were subjected to sequencing and phylogenetic analysis. Results: The seroprevalence of toxoplasmosis based on IgG titer was 35.7% in the COVID‑19 patients and 27.1% in the control group, representing not to be associated with the Toxoplasma seropositivity in COVID‑19 patients (P = 0.18) compared to healthy subjects. Anti-T. gondii IgM was not found in any of the patients and healthy individuals. According to PCR amplification of the B1 and GRA6 genes, the frequency of T. gondii in COVID-19 patients was 14.2% (10/70). However, no T. gondii infection was detected in the healthy group. The CD4+T cell count was relatively lower in toxoplasmosis-infected patients (430–450 cells/mm3) than in control group (500–1500 cells/mm3). High genetic diversity (Hd: 0.710) of the type I strain of T. gondii was characterized in the patients. Present results showed that consumption of raw vegetables and close contact with stray cats can increase the transmission of T. gondii to COVID-19 patients (P < 0.01). Conclusions: The current study revealed that T. gondii type I infection is unequivocally circulating among the COVID-19 patients in Tabriz; However, no significant association was observed between the occurrence of Toxoplasma and the severity of COVID-19. To make more accurate health decisions, multicenter investigations with a larger sample size of different ethnic groups of the Iranian population are needed. [ABSTRACT FROM AUTHOR]

Air pollution data are large-scale datasets that can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretations. So, this study aims to cluster the days of the year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatiotemporal mixture model–based clustering (STMC). Besides, mixture model–based clustering with temporal dimension (TMC) and mixture model–based clustering without considering spatiotemporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models, and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3, and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3, and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that the efficiency of clustering in air pollution data increases by considering both spatiotemporal dimensions. [ABSTRACT FROM AUTHOR]