| 1. |
- Shao, Linus Ruijin, 1964, et al.
(författare)
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From mice to women and back again: Causalities and clues for Chlamydia-induced tubal ectopic pregnancy.
- 2012
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Ingår i: Fertility and sterility. - : Elsevier BV. - 1556-5653 .- 0015-0282. ; 98:5, s. 1175-85
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Tidskriftsartikel (refereegranskat)abstract
- To provide an overview of knockout mouse models that have pathological tubal phenotypes after Chlamydia muridarum infection, discuss factors and pathological processes that contribute to inflammation, summarize data on tubal transport and progression of tubal implantation from studies in humans and animal models, and highlight research questions in the field.
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| 2. |
- Lan, Yihong, et al.
(författare)
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Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes
- 2022
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Ingår i: Emerging Themes in Epidemiology. - : BMC. - 1742-7622. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The pandemic progression is a dynamic process, in which measures yield outcomes, and outcomes in turn influence subsequent measures and outcomes. Due to the dynamics of pandemic progression, it is challenging to analyse the long-term influence of an individual measure in the sequence on pandemic outcomes. To demonstrate the problem and find solutions, in this article, we study the first wave of the pandemic—probably the most dynamic period—in the Nordic countries and analyse the influences of the Swedish measures relative to the measures adopted by its neighbouring countries on COVID-19 mortality, general mortality, COVID-19 incidence, and unemployment. The design is a longitudinal observational study. The linear regressions based on the Poisson distribution or the binomial distribution are employed for the analysis. To show that analysis can be timely conducted, we use table data available during the first wave. We found that the early Swedish measure had a long-term and significant causal effect on public health outcomes and a certain degree of long-term mitigating causal effect on unemployment during the first wave, where the effect was measured by an increase of these outcomes under the Swedish measures relative to the measures adopted by the other Nordic countries. This information from the first wave has not been provided by available analyses but could have played an important role in combating the second wave. In conclusion, analysis based on table data may provide timely information about the dynamic progression of a pandemic and the long-term influence of an individual measure in the sequence on pandemic outcomes.
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| 3. |
- Nawaz, Muhammad, et al.
(författare)
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The emerging role of extracellular vesicles as biomarkers for urogenital cancers.
- 2014
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Ingår i: Nature reviews. Urology. - : Springer Science and Business Media LLC. - 1759-4820 .- 1759-4812. ; 11, s. 688-701
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Tidskriftsartikel (refereegranskat)abstract
- The knowledge gained from comprehensive profiling projects that aim to define the complex genomic alterations present within cancers will undoubtedly improve our ability to detect and treat those diseases, but the influence of these resources on our understanding of basic cancer biology is still to be demonstrated. Extracellular vesicles have gained considerable attention in past years, both as mediators of intercellular signalling and as potential sources for the discovery of novel cancer biomarkers. In general, research on extracellular vesicles investigates either the basic mechanism of vesicle formation and cargo incorporation, or the isolation of vesicles from available body fluids for biomarker discovery. A deeper understanding of the cargo molecules present in extracellular vesicles obtained from patients with urogenital cancers, through high-throughput proteomics or genomics approaches, will aid in the identification of novel diagnostic and prognostic biomarkers, and can potentially lead to the discovery of new therapeutic targets.
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| 4. |
- Wang, Xiaoqin, Docent, 1963-, et al.
(författare)
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Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments
- 2022
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Ingår i: Statistical Methods in Medical Research. - : Sage. - 0962-2802 .- 1477-0334. ; 31:8, s. 1538-1548
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Tidskriftsartikel (refereegranskat)abstract
- A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression, treatments and a certain outcome of interest. To evaluate the performance of diagnoses, one needs not only a consistent estimation of the causal effect under a specified regime of diagnoses and treatments but also reliable confidence interval, P-value and hypothesis testing of the causal effect. In this article, we identify causal effects under various regimes of diagnoses and treatments by the point effects of diagnoses and treatments and thus are able to estimate and test these causal effects by estimating and testing point effects in the familiar framework of single-point causal inference. Specifically, using data from a Swedish prognosis study of stomach cancer, we estimate and test the causal effects on cancer survival under various regimes of diagnosing and treating hospitals including the optimal regime. We also estimate and test the modification of the causal effect by age. With its simple setting, one can readily extend the example to a large variety of settings in the area of cancer diagnosis: different personal characteristics such as family history, different diagnosing procedures such as multistage screening, and different cancer outcomes such as cancer progression.
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| 5. |
- Wang, Xiaoqin, et al.
(författare)
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Exosomes influence the behavior of human mesenchymal stem cells on titanium surfaces.
- 2020
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Ingår i: Biomaterials. - : Elsevier BV. - 1878-5905 .- 0142-9612. ; 230
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Tidskriftsartikel (refereegranskat)abstract
- Mesenchymal stem cells (MSCs) have important roles during osseointegration. This study determined (i) if MSC-derived extracellular vesicles (EVs)/exosomes can be immobilized on titanium (Ti) surfaces and influence the behavior of MSCs, (ii) if the response is differentially affected by EVs from expanded vs differentiated MSCs and (iii) if the EV protein cargos predict the functional features of the exosomes. EVs secreted by human adipose-derived MSCs were isolated by ultracentrifugation and analyzed using nanoparticle tracking analysis, Western blotting and relative quantitative mass spectrometry. Fluorescence microscopy, scanning electron microscopy, cell counting assay and quantitative polymerase chain reaction were used to analyze MSC adhesion, proliferation and differentiation. Exosome immobilization on Ti promoted MSC adhesion and spreading after 24h and proliferation after 3 and 6 days, irrespective of whether the exosomes were obtained from expansion or differentiation conditions. Immobilized exosomes upregulated stromal cell-derived factor (SDF-1α) gene expression. Cell adhesion molecules and signaling molecules were abundant in the exosomal proteome. The predicted functions of the equally-abundant proteins in both exosome types were in line with the observed biological effects mediated by the exosomes. Thus, exosomes derived from MSCs and immobilized on Ti surfaces interact with MSCs and rapidly promote MSC adhesion and proliferation. These findings provide a novel route for modification of titanium implant surfaces.
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| 6. |
- Wang, Xiaoqin, 1963-, et al.
(författare)
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Identifying and estimating net effects of treatments in sequential casual inference
- 2015
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Ingår i: Electronic Journal of Statistics. - 1935-7524. ; 9, s. 1608-1643
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Tidskriftsartikel (refereegranskat)abstract
- Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post-treatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we study identification and estimation of the net effect of each treatment in the treatment sequence. We construct a point parametrization for the joint distribution of treatments, time-dependent covariates and the outcome, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We identify net effects of treatments by their expressions in terms of point effects of treatments and express patterns of net effects of treatments by constraints on point effects of treatments. We estimate net effects of treatments through their point effects under the constraint by maximum likelihood and reduce the number of point parameters in the estimation by the treatment assignment condition. As a result, we obtain an unbiased consistent maximum-likelihood estimate for the net effect of treatment even in a long treatment sequence. We also show by simulation that the interval estimation of the net effect of treatment achieves the nominal coverage probability.
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| 7. |
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| 8. |
- Wang, Xiaoqin, 1963-, et al.
(författare)
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Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies
- 2017
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Ingår i: Journal of Applied Statistics. - : Informa UK Limited. - 0266-4763 .- 1360-0532. ; 44:14, s. 2483-2498
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Tidskriftsartikel (refereegranskat)abstract
- In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.
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| 9. |
- Wang, Xiaoqin, 1963-, et al.
(författare)
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Measuring and estimating treatment effect on dichotomous outcome of a population
- 2016
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 25:5, s. 1779-1790
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Tidskriftsartikel (refereegranskat)abstract
- In different studies for treatment effect on dichotomous outcome of a certain population, one uses different regression models, leading to different measures of the treatment effect. In observational studies, the common measures of the treatment effect are the conditional risk ratio based on a log-linear model and the conditional odds ratio based on a logistic model; in randomized trials, the common measures are the marginal risk difference based on a linear model, the marginal risk ratio based on a log-linear model, and the marginal odds ratio based on a logistic model. In this paper we express these measures in terms of the risk of a dichotomous outcome conditional on covariates and treatment, where the risk is described by a regression model. Therefore these measures do not explicitly depend on the regression model. As a result, we are able to use one regression model in one study to estimate all these measures by their maximum likelihood estimates. We show that these measures have causal interpretations and reflect different aspects of the same underlying treatment effect under the assumption of no unmeasured confounding covariate given observed covariates. We construct approximate distributions of the maximum likelihood estimates of these measures and then by using the approximate distributions we get confidence intervals for these measures. As an illustration, we estimate these measures for the effect of a triple therapy on eradication of Helicobacter pylori among Vietnamese children and are able to compare the treatment effect in this study with those in other studies.
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| 10. |
- Wang, Xiaoqin, Docent, 1963-, et al.
(författare)
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New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference
- 2020
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Ingår i: Annals of Statistics. - 0090-5364 .- 2168-8966. ; 48:1, s. 138-160
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Tidskriftsartikel (refereegranskat)abstract
- In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatmenton on the potential outcome after the last treatment. The well-known G-formula expresses these causal effects in terms of the standard paramaters. In this article, we obtain a new G-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new G-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.
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