Treating sleep disturbances in refugees and asylum seekers: results from a randomized controlled pilot trial evaluating the STARS group intervention by Britta Dumser (20698169)

“…However, there was no significant interaction with condition at post-treatment for the primary outcome (<i>d</i> = 0.29) and most secondary outcomes; the only exceptions were increased coping with nightmares, decreased daytime sleep, and time in bed.</p> <p><b>Conclusions:</b> STARS appears feasible for treating sleep disturbances in traumatized refugees in a routine clinical setting, showing moderate to large within-group effects. …”

Image 1_Natural killer cell as a potential predictive biomarker for early immune checkpoint inhibitor-associated cardiovascular adverse events: a retrospective cohort study.tif by Yujuan Wu (2249857)

“…ROC analysis identified baseline NK cell proportion as a potential predictor of CVAEs (AUC 0.674). The optimal cutoff value was determined to be 16.4%, and this finding was confirmed following PSM.…”

Image 2_Natural killer cell as a potential predictive biomarker for early immune checkpoint inhibitor-associated cardiovascular adverse events: a retrospective cohort study.tif by Yujuan Wu (2249857)

“…ROC analysis identified baseline NK cell proportion as a potential predictor of CVAEs (AUC 0.674). The optimal cutoff value was determined to be 16.4%, and this finding was confirmed following PSM.…”

Image 3_Natural killer cell as a potential predictive biomarker for early immune checkpoint inhibitor-associated cardiovascular adverse events: a retrospective cohort study.tif by Yujuan Wu (2249857)

“…ROC analysis identified baseline NK cell proportion as a potential predictor of CVAEs (AUC 0.674). The optimal cutoff value was determined to be 16.4%, and this finding was confirmed following PSM.…”

MCount has two adjustable hyperparameters, <i>d</i> and <i>λ</i>, which control the contour fineness and constrain the circle number, respectively. by Sijie Chen (1622476)

“…<p><b>(a)</b> The number of contour segments decreases as <i>d</i> increases, but excessively large <i>d</i> may lead to a failure to recognize merged colonies. …”

Validation of FGF9 expression in external database and NRK-52E cells. by Donglin Yang (490406)

“…(C) AUC value of FGF9 in the GSE30122 database. (D) The AUC value of FGF9 in the GSE96804 database. …”

Regular spiking neuron (sustained chopper) that exhibits accurate identification of envelope frequency from its spike trains. by Chris Scholes (3309477)

“…<p><b>a.</b> PSTH of the response to a pure tone at the characteristic frequency of the neuron. …”

original data.xlsx by Peng Shen (20145351)

“…CVVHDF primarily removes medium and small molecular substances, while HP can directly adsorb and remove large molecular inflammatory factors. <b>Research Objective</b>: This study aims to evaluate the efficacy of blood purification treatment (including CVVHDF combined with HP) in children with septic shock, focusing on the removal of inflammatory factors and its impact on prognosis.…”

Stable dosing regimes and linear stability analysis. by Tianyong Yao (22311889)

“…The change in mean <i>eda</i> is robust to a large range of half-lives and doses. The mean <i>eda</i> monotonically increases with both half-life and dose, with steep changes outside of the homeostatic plateau (large blue region). …”

Megafauna diversity and functional declines in Europe from the Last Interglacial to the present by Marco Davoli (14852290)

“…</p><p dir="ltr"><b>Major taxa studied: </b>Wild, large (≥10 kg) terrestrial mammals.…”

Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Table 4_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Image 2_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.tiff by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif by Haipeng Zhang (3413288)

“…A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 3_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx by Haipeng Zhang (3413288)

“…A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 1_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. …”

Image 1_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.tiff by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Data Sheet 1_Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.zip by Haoming Shen (3816988)

“…The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. …”

Data Sheet 2_Metabolomics reveals key biomarkers for ischemic stroke: a systematic review of emerging evidence.pdf by Lu Ding (475637)

“…Metabolite analysis revealed associations between decreased proline, isoleucine, valine, and alanine levels with IS. …”