METHODS: A total of 359 pregnant women living in the rural communities of Taiz governorate were enrolled in this study by house-to-house visits. Data were collected using a pre-designed questionnaire, and blood samples were collected and tested for the detection of anti- T. gondii IgM and IgG antibodies by enzyme-linked immunosorbent assay.
RESULTS: The prevalence of T. gondii infection among pregnant women in this study was 46.2% (166/359). Bivariate analysis identified the age of ≥ 30 years (odds ratio [OR] = 1.7; 95% confidence interval [CI] = 1.09-2.65, P = 0.019) and unimproved water sources (OR = 2.2; 95% CI = 1.10-4.55, P = 0.023) as factors associated with T. gondii infection among pregnant women. The multivariable analysis, however, identified unimproved water sources as an independent risk factor (adjusted OR = 2.4; 95% CI = 1.16-5.0, P = 0.018) associated with T. gondii infection among pregnant women.
CONCLUSIONS: Pregnant women in the rural communities of Taiz, Yemen are at high risk of contracting T. gondii infection. Unimproved water sources (wells, water streams and water tanks) are significantly associated with T. gondii infection and should be considered in prevention and control strategies, especially among pregnant women.
METHODOLOGY: We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.
RESULTS: We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.
CONCLUSION: We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
METHODOLOGY: The data show that the status of atmospheric environment in Malaysia, in particular in highly industrialized areas such as Klang Valley, was determined both by local and transboundary emissions and could be described as haze and non-haze periods.
RESULTS: During the non-haze periods, vehicular emissions accounted for more than 70% of the total emissions in the urban areas and have demonstrated two peaks in the diurnal variations of the aforementioned air pollutants, except ozone. The morning 'rush-hour' peak was mainly due to vehicle emissions, while the late evening peak was mainly attributed to meteorological conditions, particularly atmospheric stability and wind speed. Total suspended particulate matter was the main pollutant with its concentrations at few sites often exceeding the Recommended Malaysia Air Quality Guidelines. The levels of other pollutants were generally within the guidelines. Since 1980, six major haze episodes were officially reported in Malaysia: April 1983, August 1990, June 1991, October 1991, August to October 1994, and July to October 1997. The 1997 haze episode was the worst ever experienced by the country. Short-term observations using continuous monitoring systems during the haze episodes during these periods clearly showed that suspended particulate matter (PM10) was the main cause of haze and was transboundary in nature. Large forest fires in parts of Sumatra and Kalimantan during the haze period, clearly evident in satellite images, were identified as the probable key sources of the widespread heavy haze that extended across Southeast Asia from Indonesia to Singapore, Malaysia and Brunei. The results of several studies have also provided strong evidence that biomass burning is the dominating source of particulate matter. The severity and extent of 1997's haze pollution was unprecedented, affecting some 300 million people across the region. The amount of economic costs suffered by Southeast Asian countries during this environmental disaster was enormous and is yet to be fully determined. Among the important sectors severely affected were air and land transport, shipping, construction, tourism and agro-based industries. The economic cost of the haze-related damage to Malaysia presented in this study include short-term health costs, production losses, tourism-related losses and the cost of avertive action. Although the cost reported here is likely to be underestimated, they are nevertheless significant (roughly RM1 billion).
CONCLUSIONS: The general air quality of Malaysia since 1970 has deteriorated. Studies have shown that should no effective countermeasures be introduced, the emissions of sulfur dioxide, nitrogen oxides, particulate matter, hydrocarbons and carbon monoxide in the year 2005 would increase by 1.4, 2.12, 1.47 and 2.27 times, respectively, from the 1992 levels.
OBJECTIVE: This study was aimed to inspect the ameliorative action of A. chinensis synthesized ZnONPs against M. pneumoniae infected pneumonia mice model.
MATERIALS AND METHODS: ZnO NPs was synthesized from Albizia chinensis bark extract and characterized by UV-Vis spectroscopy, Fourier Transform Infrared (FTIR), Transmission Electron Microscopy (TEM), energy dispersive X-ray (EDX) and atomic force microscope (AFM) analyses. The antibacterial effectual of synthesized ZnONPs were examined against clinical pathogens. The pneumonia was induced to BALB/c mice via injecting the M. pneumoniae and treated with synthesized ZnONPs, followed by the total protein content, total cell counts and inflammatory mediators level was assessed in the BALF of experimental animals. The Histopathological investigation was done in the lung tissues of test animals.
RESULTS: The outcomes of this work revealed that the formulated ZnONPs was quasi-spherical, radial and cylindrical; the size was identified as 116.5 ± 27.45 nm in diameter. The in vitro antimicrobial potential of formulated ZnO-NPs displayed noticeable inhibitory capacity against the tested fungal and bacterial strains. The administration of synthesized ZnO-NPs in MP infected mice model has significantly reduced the levels of total protein, inflammatory cells, inflammatory cytokines such as IL-1, IL-6, IL-8, tumour necrosis factor-alpha (TNF-a) and transforming growth factor (TGF). Besides, the histopathological examination of MP infected mice lung tissue showed the cellular arrangements were effectively retained after administration of synthesized ZnO-NPs.
CONCLUSION: In conclusion, synthesized ZnO-NPs alleviate pneumonia progression via reducing the level of inflammatory cytokines and inflammatory cells in MP infected mice model.