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1.
目的 采用两样本孟德尔随机化研究方法探讨血清生长分化因子15(GDF15)水平与慢性淋巴细胞白血病(CLL)发生之间的关联。方法 基于欧洲人群血清GDF15和CLL的全基因组关联研究公开数据库,筛选与血清GDF15水平相关的遗传变异位点作为工具变量,采用逆方差加权法评估遗传学预测的血清GDF15浓度与CLL发生的关联,采用最大似然比法进行敏感性分析,采用MR-Egger回归探讨工具变量潜在多效性。结果 研究共纳入3个单核苷酸多态位点作为工具变量,逆方差加权法结果显示,血清GDF15水平与CLL发生风险之间存在负相关,GDF15浓度每升高一个标准差(SD),CLL发生风险降低33%(95%置信区间:2%~54%)(P=0.039)。敏感性分析得到了一致的结果。此外,MR-Egger回归未发现存在多效性。结论 本研究结果提示,在欧洲人群中,血清GDF15水平与CLL发生之间可能存在负相关,仍需大样本人群研究及体内外实验进一步阐明GDF15在CLL发生发展中的作用及其潜在生物学机制。 相似文献
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In this paper, we use optimal parameter selection technique to develop two models involving single‐vendor–multiple‐buyer supply chain, which are called the dynamic independent optimization (DIO) model and the dynamic synchronized cycles (DSC) model, respectively. These models are, respectively, similar to the traditional static independent policy model and the traditional static synchronized cycle model, except that the deterministic demands of the buyers in the above two static models are now being replaced by the stochastic demands satisfying a Wiener process, which have more real‐life applications. Similar to the above static synchronized cycles model, the synchronization of the supply chain in our DSC model is also achieved by scheduling the delivery days of the buyers and coordinating them with the vendor's production cycle. Finding the optimal expected system costs of the DIO model and the DSC model involves solving optimal parameter selection problems governed by ordinary differential equations, whose final times are continuous decision variables and discrete decision variables, respectively. Computational methods have been developed for solving these problems. Numerical results show that the coordinated policy is better than the independent optimization policy, in terms of minimizing the expected system cost of the entire supply chain. Sensitivity analysis is performed to test the effect of changing the cost coefficients and the value on the performances of these models, where is the ratio of the total mean demand rate of all the buyers over the vendor's production rate. 相似文献
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Dylan J.H.A. Henssen Richard L. Witkam Johan C.M.L. Dao Daan J. Comes Anne-Marie Van Cappellen van Walsum Tamas Kozicz Robert van Dongen Kris Vissers Ronald H.M.A. Bartels Guido de Jong Erkan Kurt 《The journal of pain》2019,20(9):1015-1026
Implantable motor cortex stimulation (iMCS) has been performed for >25 years to treat various intractable pain syndromes. Its effectiveness is highly variable and, although various studies revealed predictive variables, none of these were found repeatedly. This study uses neural network analysis (NNA) to identify predictive factors of iMCS treatment for intractable pain. A systematic review provided a database of patient data on an individual level of patients who underwent iMCS to treat refractory pain between 1991 and 2017. Responders were defined as patients with a pain relief of >40% as measured by a numerical rating scale (NRS) score. NNA was carried out to predict the outcome of iMCS and to identify predictive factors that impacted the outcome of iMCS. The outcome prediction value of the NNA was expressed as the mean accuracy, sensitivity, and specificity. The NNA furthermore provided the mean weight of predictive variables, which shows the impact of the predictive variable on the prediction. The mean weight was converted into the mean relative influence (M), a value that varies between 0 and 100%. A total of 358 patients were included (202 males [56.4%]; mean age, 54.2 ±13.3 years), 201 of whom were responders to iMCS. NNA had a mean accuracy of 66.3% and a sensitivity and specificity of 69.8% and 69.4%, respectively. NNA further identified 6 predictive variables that had a relatively high M: 1) the sex of the patient (M = 19.7%); 2) the origin of the lesion (M = 15.1%); 3) the preoperative numerical rating scale score (M = 9.2%); 4) preoperative use of repetitive transcranial magnetic stimulation (M = 7.3%); 5) preoperative intake of opioids (M = 7.1%); and 6) the follow-up period (M = 13.1%). The results from the present study show that these 6 predictive variables influence the outcome of iMCS and that, based on these variables, a fair prediction model can be built to predict outcome after iMCS surgery.PerspectiveThe presented NNA analyzed the functioning of computational models and modeled nonlinear statistical data. Based on this NNA, 6 predictive variables were identified that are suggested to be of importance in the improvement of future iMCS to treat chronic pain. 相似文献
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Robert C. Gosselin 《International journal of laboratory hematology》2021,43(Z1):109-116
There are many preanalytical variables (PAV) that are known to affect coagulation testing. The more commonly acknowledged PAV addressed by the clinical laboratory tend to start with their influence on blood collection, but realistically coagulation PAV starts with the patient, where the laboratory has less influence or control. Patient selection and appropriate timing for blood collection may be integral for assuring proper diagnosis and management. Laboratory control and assurance for ideal phlebotomy practice would mitigate most PAVs related to blood collection to minimize suboptimal sample collection. Laboratory oversight of sample transportation, processing and storage will assure sample integrity until testing can be facilitated. The purpose of this document is to review common PAV that should be taken into consideration when ordering, performing and interpreting a coagulation test result, with additional attention to the effect of direct oral anticoagulants (DOACs). 相似文献
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Eri Hagiwara Takuma Katano Kohsuke Isomoto Ryota Otoshi Hideaki Yamakawa Ryo Okuda Akimasa Sekine Tomohisa Baba Shigeru Komatsu Takashi Ogura 《Respiratory investigation》2019,57(1):54-59
Background
The incidence of asymptomatic pulmonary Mycobacterium avium complex (MAC) disease appears to be increasing. This study aimed to determine the clinical characteristics and examine early outcomes of patients newly diagnosed with MAC disease.Methods
We retrospectively reviewed the medical records of all 184 patients newly diagnosed with MAC disease between April 2013 and March 2015 at our hospital. Culture conversion, defined as at least two consecutive negative cultures, was used as the early outcome measure.Results
Of 184 patients, 45 were male and 139 were female, with a mean age of 70 years. Abnormal chest shadow found during an annual health check-up (58 patients) or incidentally during visits for other diseases (72 patients), was the major reason for referral to hospital, typically with no symptoms. Anti-MAC IgA antibody was positive in 64.5% of patients, and the positive rate was associated with the extent of lesions. Clarithromycin-based multi-drug chemotherapy was initiated in 111 patients. Culture conversion was achieved in 61 of 82 (74.4%) patients who were able to continue multi-drug chemotherapy. Patients who achieved culture conversion were more likely to be younger, female, and have a lower smear grade and nodular-bronchiectatic type. Multivariate logistic regression analysis identified patient age and smear grade as predictive variables.Conclusions
Abnormal chest shadow found during health check-up was the major reason for hospital visits, and most were asymptomatic. Culture conversion was achieved in three-fourths of patients treated, and was associated with age and smear grade, supporting early intervention at a younger age. 相似文献7.
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Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations. 相似文献
9.
This paper estimates the causal impact of retirement on the Body Mass Index (BMI) of adults aged 50–69 years old, on the probability of being either overweight or obese and on the probability of being obese. Based on the 2004, 2006 and 2010–2011 waves of the Survey of Health, Ageing and Retirement in Europe (SHARE), our identification strategy exploits variation in European Early Retirement Ages (ERAs) and stepwise increases in ERAs in Austria and Italy between 2004 and 2011 to examine an exogenous shock to retirement behavior. Our results show that retirement induced by discontinuous incentives in early retirement schemes causes a 12-percentage point increase in the probability of being obese among men within a two- to four-year period. We find that the impact of retirement is highly non-linear and mostly affects the right-hand side of the male BMI distribution. Additional results show that this pattern is driven by men retiring from strenuous jobs and by those who were already at risk of obesity. In contrast, no significant results are found among women. 相似文献
10.
How to regress and predict in a Bland–Altman plot? Review and contribution based on tolerance intervals and correlated‐errors‐in‐variables models 下载免费PDF全文
Two main methodologies for assessing equivalence in method‐comparison studies are presented separately in the literature. The first one is the well‐known and widely applied Bland–Altman approach with its agreement intervals, where two methods are considered interchangeable if their differences are not clinically significant. The second approach is based on errors‐in‐variables regression in a classical (X,Y) plot and focuses on confidence intervals, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors. This paper reconciles these two methodologies and shows their similarities and differences using both real data and simulations. A new consistent correlated‐errors‐in‐variables regression is introduced as the errors are shown to be correlated in the Bland–Altman plot. Indeed, the coverage probabilities collapse and the biases soar when this correlation is ignored. Novel tolerance intervals are compared with agreement intervals with or without replicated data, and novel predictive intervals are introduced to predict a single measure in an (X,Y) plot or in a Bland–Atman plot with excellent coverage probabilities. We conclude that the (correlated)‐errors‐in‐variables regressions should not be avoided in method comparison studies, although the Bland–Altman approach is usually applied to avert their complexity. We argue that tolerance or predictive intervals are better alternatives than agreement intervals, and we provide guidelines for practitioners regarding method comparison studies. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献