Ybx1 regulation of its target mRNAs is m⁵C-depdendent (Related to Fig 5). by Jian Zhang (1682)

“…<p><b>(A)</b> RT-qPCR confirmed the decreased expression levels of Ybx1 target mRNAs in neural stem cells with Ybx1 knockdown using siRNA. …”

Table 1_COVID-19 pandemic increased ESKAPEEc bloodstream infections and amplified carbapenem resistance in Chinese children: a multicenter surveillance study (2016–2023).xls by Xiaoqiang Li (612879)

“…Between 2016–2019 and 2020–2023, resistance to ceftazidime and gentamicin decreased in Escherichia coli and Klebsiella pneumoniae while resistance to imipenem and meropenem increased. …”

Table2_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

Table13_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

Minimal test data set by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Data Sheet 1_Perceived benefits and barriers to the use of long-acting injectable antiretroviral treatment among adolescents and young people living with HIV in Western Kenya: qual... by Shukri A. Hassan (14245301)

“…Introduction<p>Adolescents and young people living with HIV (AYPLHIV) face significant hurdles in adhering to daily oral antiretroviral therapy (ART). …”

Table12_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

OSNet network structure. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Data Sheet 2_Perceived benefits and barriers to the use of long-acting injectable antiretroviral treatment among adolescents and young people living with HIV in Western Kenya: qual... by Shukri A. Hassan (14245301)

“…Introduction<p>Adolescents and young people living with HIV (AYPLHIV) face significant hurdles in adhering to daily oral antiretroviral therapy (ART). …”

Table3_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

Table9_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

Image1_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.tif by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

Table7_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

DataSheet_1_FCGR2/3 polymorphisms are associated with susceptibility to Kawasaki disease but do not predict intravenous immunoglobulin resistance and coronary artery aneurysms.docx by Paula Uittenbogaard (19691794)

“…Treatment with intravenous immunoglobulin (IVIg) significantly decreases the risk of CAA, possibly through competitive binding to Fc-gamma receptors (FcγRs), which reduces the binding of pathological immune complexes. …”

Table 1_Association of MIF rs1007888 and ARAP1 rs1552224 genetic variants with the risk of gestational diabetes mellitus in a chinese population; case study and meta-analysis.xlsx by Yuxuan Zhang (1425721)

“…ARAP1 rs1552224 was significantly linked to reduced GDM incidence (Allele Model A vs. …”

Table6_Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization.xlsx by Yifang He (18126257)

“…</p>Conclusions<p>Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.…”

DataSheet1_Effects of mesenchymal stromal cells and human recombinant Nerve Growth Factor delivered by bioengineered human corneal lenticule on an innovative model of diabetic reti... by Letizia Pelusi (9606879)

“…In the last years, mesenchymal stromal cells (MSCs) and neurotrophins like Nerve Growth Factor (NGF), have garnered significant attention as innovative therapeutic approaches targeting DR-associated neurodegeneration. …”

Table 2_Association of MIF rs1007888 and ARAP1 rs1552224 genetic variants with the risk of gestational diabetes mellitus in a chinese population; case study and meta-analysis.xlsx by Yuxuan Zhang (1425721)

“…ARAP1 rs1552224 was significantly linked to reduced GDM incidence (Allele Model A vs. …”

YOLOv8 overall framework. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

The performance of YOFGD model on MOT16. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”