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ObjectivesThe relationship between statin treatment and fracture risk is still controversial, especially in in patients with cardiovascular diseases (CVDs). We aim to determine whether statin therapy affects the occurrence of fractures in the general US population and in patients with CVDs.MethodsEpidemiological data of this cross-sectional study were extracted from the National Health and Nutrition Examination Survey (NHANES, 2001-2020, n = 9,893). Statins records and fracture information were obtained from the questionnaires. Weighted logistic regressions were performed to explore the associations between statin and the risk of fracture.ResultsStatin use was found to be associated with reduced risk of fracture mainly in male individuals aged over 50 years old and taking medications for less than 3 years, after adjusted for confounders including supplements of calcium and vitamin D. The protective effects were only found in subjects taking atorvastatin and rosuvastatin. We found null mediation role of LDL-C and 25(OH)D in such effects. Statin was found to reduce fracture risk in patients with cardiovascular diseases (CVDs, OR: 0.4366, 95%CI: 0.2664 to 0.7154, P = 0.0014), and in patients without diabetes (OR: 0.3632, 95%CI: 0.1712 to 0.7704, P = 0.0091).ConclusionsStatin showed advantages in reducing risk of fracture in male individuals aged over 50 years old and taking medications for less than 3 years. More research is needed to determine the impact of gender variations, medication duration, and diabetes.

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

A novel dynamic model covering five types of cells and three connected compartments, peripheral blood (PB), lymph nodes (LNs), and the central nervous system (CNS), is here proposed. It is based on assessment of the biological principles underlying the interactions between the human immunodeficiency virus type I (HIV-1) and the human immune system. The simulated results of this model matched the three well-documented phases of HIV-1 infection very closely and successfully described the three stages of LN destruction that occur during HIV-1 infection. The model also showed that LNs are the major location of viral replication, creating a pool of latently infected T4 cells during the latency period. A detailed discussion of the role of monocytes/macrophages is made, and the results indicated that infected monocytes/macrophages could determine the progression of HIV-1 infection. The effects of typical highly active antiretroviral therapy (HAART) drugs on HIV-1 infection were analyzed and the results showed that efficiency of each drug but not the time of the treatment start contributed to the change of the turnover of the disease greatly. An incremental count of latently infected T4 cells was made under therapeutic simulation, and patients were found to fail to respond to HAART therapy in the presence of certain stimuli, such as opportunistic infections. In general, the dynamics of the model qualitatively matched clinical observations very closely, indicating that the model may have benefits in evaluating the efficacy of different drug therapy regimens and in the discovery of new monitoring markers and therapeutic schemes for the treatment of HIV-1 infection.