Change in the number of Iba-1-positive cells in the dorsal root ganglia (DRG) of PSNL-exposed rats and the effect of treatment with MSCs. by Kanako Miyano (3805288)

Chlorinated Cubane-1,4-dicarboxylic Acids by Adéla Křížková (14551641)

Temsirolimus does not impact HGPS mitochondrial dysfunction. by Diana Gabriel (3608093)

Fig 1 - by Ruud F. W. Franssen (12484599)

Percentage of strong-positively stained sperm for calbindin immunostaining (METH vs. control). by Paweena Kaewman (14329011)

Relative optical density of calbindin expression levels in rat sperm (METH vs. control). by Paweena Kaewman (14329011)

TSG101 is associated with PEAV macropinocytosis. by Xiongnan Chen (13307178)

Determination of the survival rate and neurite outgrowth of the SGN following exposure to Na₂(PtCl₆). by Elisabeth Berger (2271022)

Results from secondary outcomes of questionnaires. by Isabella Hotz (18831177)

Segmentation tool performance on real and simulated motion-corrupted data. by Hampus Olsson (18389522)

Effects of AD- and UC-MSCs on PSNL-induced pain. by Kanako Miyano (3805288)

Representative FACS quantitative analyses showing FDNVs-induced apoptosis. by Saharut Wongkaewkhiaw (9853495)

PEDV entry dependent on ALIX, while PEAV entry requires ALIX and TSG101. by Xiongnan Chen (13307178)

Baseline characteristics for pneumonia. by Mathias Aagaard Christensen (19139443)

“…<div><p>Introduction</p><p>Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. …”

Protein-protein interaction modeling reveals a putative CXCL10 binding site on GP63 that closely approximates the GP63 active site and CXCL10 cleavage site. by Alejandro L. Antonia (7147739)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”