Table_1_Impact of free hypertension pharmacy program and social distancing policy on stroke: A longitudinal study.DOCX

BackgroundThe estimated lifetime risk of stroke was the highest in East Asia worldwide, especially in China. Antihypertensive therapy can significantly reduce stroke mortality. However, blood pressure control is poor. Medication adherence is a barrier as patients’ out-of-pocket costs have risen. We aimed to take advantage of a free hypertension pharmacy intervention and quantified the impact on stroke mortality.MethodsA free pharmaceutical intervention program was implemented in Deqing, Zhejiang province in April 2018. Another non-pharmaceutical intervention, social distancing due to the pandemic of Coronavirus disease 2019 (COVID-19), was also key to affecting stroke mortality. We retrospectively collected the routine surveillance data of stroke deaths from Huzhou Municipal Center for Disease Prevention and Control in 2013–2020 and obtained within-city mobility data from Baidu Migration in 2019–2020, then we quantified the effects of both pharmaceutical intervention and social distancing using Serfling regression model.ResultsCompared to the predicted number, the actual number of stroke deaths was significantly lower by 10% (95% CI, 6–15%; p ConclusionFree hypertension pharmacy program has great potential to prevent considerable stroke deaths. In the future, the free supply of low-cost, essential medications that target patients with hypertension at increased risk of stroke could be taken into account in formulating public health policies and guiding allocations of health care resources.</p

Additional file 1 of Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study

Supplementary Material

The parameters for two different genes under amplitude modulation and frequency modulation.

<p>The parameters for two different genes under amplitude modulation and frequency modulation.</p

Co-localization between genes and transcription factors.

<p>(<b>A</b>) Experimental results with immuno-RNA FISH reveal co-localization between transcription factors and factories (RNAPII), genes and transcription factors, and gene pairs and transcription factors. This is the contour plot from original experimental data. (Aa) Immune-fluorescence detection of Klf1 (red) and RNAPII-S5P (green) in definitive erythroid cells, with a scale bar of 2 µm. This shows the co-localization between Klf1 and transcription factories RNAPII. This Klf1 background association rate (level) is estimated to be 20%. (Ab) The co-localization between transcription factor Klf1 and Hbb gene. (Ac) The co-localization between factor Klf1 and genes pairs (Hbb and Hist1). (<b>B</b>) Simulation results when we hold the Klf1 background association level as 20%, while the translocation of transcription units (genes, factors and factories) are following sub-diffusion process (<i>H</i> = 0.4). (Ba) Gene-factor association level (numbers indicated behind the stars) with various factors and binding time, both for 2D and 3D cases. The number of genes (for each family) is fixed to be 5 and the Klf1 (X factor) background association level is fixed to be 20% (the stars indicate the parameter values when this condition is satisfied). The detailed Klf1 association level for each X gene and Y gene are presented in the figure below, revealing the fact that the simulation results for 3D case (gene-factor association level = 0.6) match the experimental result (gene-factor association level = 0.64) quite well for a specific set of parameters (5 genes, 2 factors and 130 sec binding time). (Bb) Gene-factor association level (numbers besides the stars) with various genes and binding time for 2D and 3D cases. The number of factors is fixed to be 2 and the Klf1 background association level is fixed to be 20%. (Bc) The association rate of Klf1 (X factor) with Z gene and paired X-Z genes when there is a negative correlation between X and Z gene, or X and Z factors, under 3D case. The parameter used here are 5 genes, 2 factors, and 130 sec binding time. Preventing probability means the chance for stopping another gene (factor) to enter the factory when there is already one gene (factor) in that factory. When <i>p</i> = 0, it represents the independent situation of X factor (gene) and Y factor (gene).</p

The simulation results for ratio of co-localization in flattened and spherical nucleus.

<p>(<b>A</b>) The co-localization ratio increases as the degree of flatness of the nucleus increases when there are 5 factors and 10 sec binding time, and is independent of the number of transcription factories, at least in the flattened nucleus, such as E10 (embryonic blood), E14 (fetal liver erythroid), AS (adult anemic spleen erythroid), Sp (normal adult spleen), Th (adult thymus) , Br (fetal brain), mouse embryonic fibroblasts (MEFs) in experiments. Scale bar = 10 µm. The cubic of spherical nucleus and rectangular block of flatten nucleus demonstrate the positioning of transcription factories, factors and genes (refer to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002094#pcbi-1002094-g003" target="_blank">Fig. 3A</a>). Note that the volumes of the cubic spherical nucleus and the rectangular flattened block are either the same (solid line), or the volume of the flattened nucleus is 5 times bigger than that of the spherical nucleus (dash line). (<b>B</b>) The co-localization ratio is a decreasing function of the number of transcription factors for both flattened nucleus of the same volume as the spherical one, and the nucleus of 5 times larger volume. No matter the volume, the co-localization ratio is independent of the transcription factories.</p

Parameter values used for 2D and 3D Brownian motion and fractional Brownian motion.

<p>Parameter values used for 2D and 3D Brownian motion and fractional Brownian motion.</p

Simulation results for 3D case, similar as Fig. 2.

<p>(<b>A</b>) Demonstration of the 3D framework in a cubic, where the big green dots represent the location of the transcription factories, asteroids the transcription factors, and small dots the genes. Different families are represented by either red or blue. (<b>B</b>) The inter-co-localization interval distribution for <i>T_{c, xx}</i> and <i>T_c,xy</i>. The parameters are the same as in (D). (<b>C</b>) The mean values of ICI distributions by varying the factor number and binding time between genes and factors. The mean ICI increases as the binding time increases, and decreases as the factor number increases. (<b>D</b>) The distribution of the co-localization ratio for X-X genes and X-Y genes. (<b>E</b>) The colicalization ratio for various combinations of factor numbers and binding times. The critical ratio is 0.52. The red dashed curve is the threshold boundary to distinguish whether the co-localization is significant or tends to be random.</p

Co-localization regions for sub- and supperdiffusion Brownian motions.

<p>(<b>A</b>) The inter-co-localization intervals for super- and sub-diffusion for 5 transcription factors and 10 sec binding time. (<b>B</b>) Co-localization regions for different factors and binding times for sub-diffusion case (<i>H</i> = 0.1) and super-diffusion case (<i>H</i> = 0.9) in 2D. (C) Co-localization ratio versus the Hurst index <i>H</i> in 3D.</p

Dendritic Cells Pulsed with Leukemia Cell-Derived Exosomes More Efficiently Induce Antileukemic Immunities

<div><p>Dendritic cells (DCs) and tumor cell-derived exosomes have been used to develop antitumor vaccines. However, the biological properties and antileukemic effects of leukemia cell-derived exosomes (LEXs) are not well described. In this study, the biological properties and induction of antileukemic immunity of LEXs were investigated using transmission electron microscopy, western blot analysis, cytotoxicity assays, and animal studies. Similar to other tumor cells, leukemia cells release exosomes. Exosomes derived from K562 leukemia cells (LEX_K562) are membrane-bound vesicles with diameters of approximately 50–100 μm and harbor adhesion molecules (<i>e.g.</i>, intercellular adhesion molecule-1) and immunologically associated molecules (<i>e.g.</i>, heat shock protein 70). In cytotoxicity assays and animal studies, LEXs-pulsed DCs induced an antileukemic cytotoxic T-lymphocyte immune response and antileukemic immunity more effectively than did LEXs and non-pulsed DCs (<i>P</i><0.05). Therefore, LEXs may harbor antigens and immunological molecules associated with leukemia cells. As such, LEX-based vaccines may be a promising strategy for prolonging disease-free survival in patients with leukemia after chemotherapy or hematopoietic stem cell transplantation.</p></div

Exosome-uptaking by dendritic cells (a) Carboxyfluorescein succinimidyl ester (CFSE)-labeled exosomes were co-cultured <i>in vitro</i> with dendritic cells (DCs), and CFSE-positive DCs were detected using flow cytometry at different times during the culture.

<p>Confocal microscopy was used concurrently with flow cytometry to visualize the cultured DCs. (b) Phase changes of CFSE expression in DCs at different time points during the culture. (c) To investigate the rate of decay of exosomes (EXOs) in DCs, DCs were incubated with CFSE-labeled EXOs for 4 h, washed twice with phosphate-buffered saline, cultured in culture medium, and examined at different time points for up to 72 h.</p