Characteristics of JIA patients. by Yasmine Makhlouf (17409866)

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List of excluded articles. by Yasmine Makhlouf (17409866)

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Growth assessment parameters before and after biological treatment in the different studies. by Yasmine Makhlouf (17409866)

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Sound stress exposure prolonged the period of decreased withdrawal threshold after complete Freund’s adjuvant (CFA) injection. by Satoka Kasai (3861115)

“…(B) 50% withdrawal threshold in CFA-treated mice exposed to sound stress. They showed a significant decrease in 50% withdrawal threshold on day 7 after CFA injection, and CFA-treated mice exposed to sound stress showed a significant decrease in 50% withdrawal threshold during days 7–21 after CFA injection (CFA, on day 7, ****<i>P</i> < 0.001 vs day 0, on day 10, ***<i>P</i> < 0.001 vs day0; CFA + stress, day 7, 10, 14, and 21, ****<i>P</i> < 0.0001 vs day 0, Dunnett’s test). …”

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Methodological flowchart. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Preference for the EIA vs. ETA across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

FBP primarily decreases the protein level of HMGB1 by promoting its lysosomal degradation upon viral infection. by Penghui Hu (695020)

Decreased synthesis of VCAM-1, tubulin and dynein proteins in the heart of mice stimulated with EVs (green) or ICs (blue). by Alberto Cornet-Gomez (10676133)

“…The values represent the mean ± SEM, with significance set at p < 0.05 (*).…”

Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Charge-Transfer-Driven Electrical Conductivity in Single Crystals of Assembled Triphenylamine Bis-urea Macrocycles by Fahidat A. Gbadamosi (22123930)

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Cells lacking Cbf11 and/or Mga2 display nutrient-responsive decrease in lipid droplet (LD) content. by Anna Marešová (14375187)