<i>GADD45a</i> promoter activity and functional promoter region in response to mechanical stress. by Sumegha Mitra (579813)

“…<p>(<b>A</b>) Human pulmonary artery endothelial cells (EC) were plated on Bioflex stretch plates and transfected with a full-length <i>GADD45a</i> promoter vector followed by cyclic stretch (CS, 5% or 18%) for 4h. …”

Transcranial Direct Current Stimulation (tDCS) modulates motor execution in a limb reaching task. by Thomas Zandonai (12813623)

“…Here we tested 16 participants in a quick pointing action after 20 minutes (2mA) of transcranial Direct Current Stimulation (tDCS) delivered at the Supplementary Motor Area (SMA) in a single-blind crossover design study for testing the feedforward components in the control of action. tDCS stimuli were delivered in three randomized sessions of stimulations as anodal, cathodal and sham as a control. …”

A randomized controlled trial of Roux-en-Y gastrojejunostomy vs. gastroduodenostomy with respect to the improvement of type 2 diabetes mellitus after distal gastrectomy in gastric... by Yoon Young Choi (113840)

“…Sixty percent of RY patients and 20% of BI patients decreased their medication satisfying FBS<126 mg/dL and HbA1c<6.5% and 5% of BI patients stopped their medication satisfying the criteria of FBS<126 mg/dL and HbA1c<6.0%. …”

Differential chemokine expression under the control of peripheral blood mononuclear cells issued from Alzheimer’s patients in a human blood brain barrier model by Julie Vérité (5616848)

“…PBMCs from moderate AD patients decreased CCL2 and CCL5 levels in endothelial, and also CXCL10 in abluminal compartments and in PBMCs compared to PBMCs from mild AD patients. …”

vRNA in brain regions controlling cognitive function and within CNS border tissues. by Sonny R. Elizaldi (9684796)

“…<p><b>(A)</b> Acute 251 cohort (n = 4) assessed<b>. (B)</b> SIV vRNA (<b>C)</b> SIV vDNA (copies/10^6 cells) in brain regions specified (RT-qPCR on post-mortem punch biopsies from specified regions. …”

miRNA-524-5p was target of nifedipine and regulated the expression of BRI3. by Dong-Qing Guo (667396)

“…<p>a) Predicted duplex formation between human BRI3 3′-UTR (top) and three miRNA (bottom) including has-miRNA-524-5p, hsa-miR-1224-3p, hsa-miR-1229 by using TargetScan software. b) The expression of miRNA-524-5p decreased in the MDA-MB-231 cells treated with nifedipine for different periods. …”

Image_2_Adding physical activity to intensive trauma-focused treatment for post-traumatic stress disorder: results of a randomized controlled trial.pdf by Eline M. Voorendonk (10130082)

“…Clinician and self-reported PTSD symptoms significantly decreased for both conditions, with large effect sizes (e.g., CAPS-5 d_pre-post = 2.28). …”

Image_1_Adding physical activity to intensive trauma-focused treatment for post-traumatic stress disorder: results of a randomized controlled trial.pdf by Eline M. Voorendonk (10130082)

“…Clinician and self-reported PTSD symptoms significantly decreased for both conditions, with large effect sizes (e.g., CAPS-5 d_pre-post = 2.28). …”

Table_1_Adding physical activity to intensive trauma-focused treatment for post-traumatic stress disorder: results of a randomized controlled trial.pdf by Eline M. Voorendonk (10130082)

“…Clinician and self-reported PTSD symptoms significantly decreased for both conditions, with large effect sizes (e.g., CAPS-5 d_pre-post = 2.28). …”

Development of a multivariable prediction model for identification of patients at risk for medication transfer errors at ICU discharge by Liesbeth B. E. Bosma (6650735)

“…TC=4159" target="_blank">NTR4159</a>, 5 September 2013, retrospectively registered.…”

Image_3_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.TIF by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_1_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_3_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_7_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Image_1_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.TIF by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_2_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_4_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Table_6_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.XLSX by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Image_2_Dynamic changes of fecal microbiota in a weight-change model of Bama minipigs.TIF by Bo Zeng (428742)

“…The diversity and community structures of fecal microbiota (418 samples) was investigated by using 16S rRNA (V3-V4) high-throughput sequencing.</p>Results<p>During the weight gain period (1~27 week), the alpha diversity of fecal microbiota exhibited a “down-up-down” fluctuations, initially decreasing, recovering in the mid-term, and decreasing again in the later stage. …”

Data Sheet 1_Identification of the malonylation modification in Staphylococcus aureus and insight into the regulators in biofilm formation.xlsx by Xiaoyan Yu (319080)

“…Molecular docking analysis found that antimicrobial peptide BCp12 and natural compound chlorogenic acid could bind with the malonylation sites in AgrA (ΔG = −6.888 and −5.302 kcal/mol, respectively).…”