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- Heidari, S, et al.
(författare)
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The effect of lead exposure on IQ test scores in children under 12 years: a systematic review and meta-analysis of case-control studies
- 2022
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Ingår i: Systematic reviews. - : Springer Science and Business Media LLC. - 2046-4053. ; 11:1, s. 106-
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Tidskriftsartikel (refereegranskat)abstract
- An inevitable exposure to the toxic heavy metal such as lead in our environmental can have irreversible effects on children’s mental performance.In this study, 3316 children in 8 case-control studies were selected for review. The case group was exposed to a concentration of lead above 10 μg/dL, and the control group was exposed to a concentration of less than 10 μg/dL, but the duration of exposure was different among studies, and the subgroup analysis was performed based on this variable.In the subgroup with duration of exposure less than the average of 4.5 years, the difference of IQ test score between two groups was significant (MD = −3.53) (P-value <0.05). Also, in the subgroup with more than 4.5 years of duration, the difference of IQ test score was significant (MD = −22.63) (P-value < 0.001).This study demonstrates that the concentration and duration of lead exposure have a large effect on mental function in children.
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- Hoang, M. T., et al.
(författare)
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Effects of the COVID-19 Pandemic on the Number of New Dementia Diagnoses and the Quality of Dementia Diagnostics and Treatment
- 2024
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Ingår i: JPAD-JOURNAL OF PREVENTION OF ALZHEIMERS DISEASE. - 2274-5807 .- 2426-0266.
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundCare trajectories were disrupted during the COVID-19 pandemic. However, how COVID-19 influenced the number of new dementia diagnoses, and the quality of dementia work-ups, and treatment is understudied.ObjectiveTo investigate the change in new dementia registrations, diagnostics, and treatment in the pre-, COVID-19 and post-COVID-19 pandemic periods.DesignA nationwide cohort study.SettingThis population-based study used data from the Swedish Registry for Cognitive/Dementia disorders - SveDem, and other nationwide registries in Sweden.ParticipantsPersons with dementia diagnosed between 2019 and 2021 were divided into three groups based on the date of diagnosisthe pre-COVID-19 period (01 January 2019 - 29 February 2020), the COVID-19 period (01 March 2020 - 31 December 2020), and the post-COVID-19 period (01 January 2021 - 31 August 2021).ParticipantsPersons with dementia diagnosed between 2019 and 2021 were divided into three groups based on the date of diagnosisthe pre-COVID-19 period (01 January 2019 - 29 February 2020), the COVID-19 period (01 March 2020 - 31 December 2020), and the post-COVID-19 period (01 January 2021 - 31 August 2021).MeasurementsOutcomes included dementia diagnostics and treatments.ResultsThe monthly average number of new dementia cases registered in SveDem was 595, 415 and 470, respectively in the pre-COVID-19, COVID-19 and post-COVID-19 period. Compared to the pre-COVID-19 period, the monthly number of registrations decreased, but provision of the basic diagnostic work-up, its individual tests, and the use of cholinesterase inhibitors, memantine and antipsychotics were not significantly different in the COVID-19 period. Compared to the pre-COVID-19 period, new dementia diagnoses continued to be low in the post-COVID-19 period, but diagnosed individuals were more likely to receive the complete basic diagnostic work-up (OR 1.14, 95% CI 1.00-1.29), blood analysis (OR 1.88, 95% CI 1.44-2.49), computed tomography and magnetic resonance imaging (OR 1.22, 95% CI 1.01-1.48), occupational therapy assessment (OR 1.13, 95% CI 1.04-1.22), and memantine (OR 1.19, 95% CI 1.07-1.31).ConclusionThe quantity of new dementia registrations in SveDem decreased in the COVID-19 period and has not returned to pre-COVID-19 levels, but the quality of the work-ups which were conducted and registered in SveDem was similar or higher than in the pre-COVID-19 period. It is imperative to implement policies to increase SveDem registration with the aim of matching or exceeding pre-COVID-19 levels.
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- Moghoofei, M, et al.
(författare)
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Bacterial and viral coinfection in idiopathic pulmonary fibrosis patients: the prevalence and possible role in disease progression
- 2022
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Ingår i: BMC pulmonary medicine. - : Springer Science and Business Media LLC. - 1471-2466. ; 22:1, s. 60-
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive interstitial pneumonia of unknown aetiology with a mean survival rate of less than 3 years. No previous studies have been performed on the role of co-infection (viral and bacterial infection) in the pathogenesis and progression of IPF. In this study, we investigated the role of viral/bacterial infection and coinfection and their possible association with pathogenesis and progression of IPF.MethodsWe investigated the prevalence and impact of bacterial and viral coinfection in IPF patients (n = 67) in the context of pulmonary function (FVC, FEV1and DLCO), disease status and mortality risk. Using principal component analysis (PCA), we also investigated the relationship between distribution of bacterial and viral co-infection in the IPF cohort.ResultsOf the 67 samples, 17.9% samples were positive for viral infection, 10.4% samples were positive for bacterial infection and 59.7% samples were positive coinfection. We demonstrated that IPF patients who were co-infected had a significantly increased risk of mortality compared (p = 0.031) with IPF patients who were non-infected [Hazard ratio: 8.12; 95% CI 1.3–26.9].ConclusionIn this study, we report for the first time that IPF patients who were coinfected with bacterial and viral infection have significantly decreased FVC and DLCO(% predicted). Besides, the results demonstrated the increased AE-IPF, increased incidence of death and risk of mortality in infected/coinfected patients compared to non-infected IPF patients.
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- Shiri, I, et al.
(författare)
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High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms
- 2022
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 14817-
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Tidskriftsartikel (refereegranskat)abstract
- We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805–0.887) and 0.807 (0.752–0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.
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