N-acetilcysteine prevents the <i>A</i>. <i>simplex</i> CE-induced TEER decrease. by Noelia Carballeda-Sangiao (2889611)

Transepithelial resistance of NHBE decreases significantly after PAA exposure. by Ellie S. Burns (21246537)

JNJ26993135 + BML-111 decreases permeability and enhances ZO-1 and claudin-1 induced by PAF but does not change TEER. by Kameljit K. Kalsi (16496511)

YOLO11. by Junyan Wang (4738518)

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SCI-YOLO11. by Junyan Wang (4738518)

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GXM decreases the trans-endothelial electrical resistance (TEER) and transmigration of <i>C</i>. <i>neoformans</i> through HBECs in a blood brain barrier (BBB) model. by Hiu Ham Lee (15374677)

BA attenuated the decrease in the expression of ZO-1 and Occludin induced by LPS in Caco2 cells. by Luqiong Liu (11537092)

Structure of the SCI-YOLO11 network. by Junyan Wang (4738518)

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BA attenuated the decrease in the integrity and increase in the permeability of the epithelial barrier injury induced by LPS in Caco2 cell monolayers. by Luqiong Liu (11537092)

Y-27632 collaborated with BA to attenuate the increase in the integrity and decrease in the permeability of epithelial barrier injury induced by LPS in Caco2 monolayers. by Luqiong Liu (11537092)

“…<p>(<b>A)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on days 1–22. …”

Y-27632 synergized with BA to alleviate the decrease in the expression of and abnormal localization of ZO-1 and Occludin in LPS-induced Caco2 cells. by Luqiong Liu (11537092)

The network structure of FasterNet--YOLO. by Shiwei Yu (6060308)

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Treatment with ICs causes a significant decrease in podocyte proliferation. by George Haddad (470789)

Detection effect diagram of YOLO11. by Jianming Meng (22209039)

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PR diagram of YOLOv8n and BGS-YOLO: (a) YOLOv8n and (b) BGS-YOLO. by Zeyu Liang (4813593)

The architecture of the proposed BGM-YOLO model. by Chenghai Yu (21440036)

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Overall network architecture of FCMI-YOLO. by Junjie Lu (160350)

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Structure of UAS-YOLO network. by Ju Liang (4277062)

“…These characteristics impose high demands on detection algorithms in terms of fine-grained feature extraction, cross-scale fusion capability, and occlusion resistance.The YOLOv11s model has significant limitations in practical applications: its feature extraction module has a single semantic representation, the traditional feature pyramid network has limited capability to detect multi-scale targets, and it lacks an effective feature compensation mechanism when targets are occluded.To address these issues, we propose a UAV aerial small target detection algorithm named UAS-YOLO (Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention module YOLO), which incorporates three key optimizations. …”

Transepithelial electrical resistance (TEER) (N = 6). by Shirko Marcel Shokr (19173337)

“…<p><b>(A)</b> During the cultivation of SMC and ALI we observed significantly differences on day 18 (SMC: 9.61 kΩ*cm²; ALI: 7.73 kΩ*cm²; p<0.05) and day 25 (SMC: 8.19 kΩ*cm²; ALI: 6.44 kΩ*cm²; p<0.05) ALI cultures showed significantly decreased values compared to SMC. …”

Comparison of YOLO-DSTD model with other models. by Jianming Meng (22209039)

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