Image 2_Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma.tif by Lichao Cao (276107)

“…Clinicopathologic characteristics were significantly correlated with risk scores. There were significant differences in the proportion of tumor-infiltrating immune cells, the TP53 mutation rate, the expression of immune checkpoint-related genes, and IC50 of the chemotherapeutic drugs between the low- and high-risk groups. …”

Table 1_Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma.docx by Lichao Cao (276107)

“…Clinicopathologic characteristics were significantly correlated with risk scores. There were significant differences in the proportion of tumor-infiltrating immune cells, the TP53 mutation rate, the expression of immune checkpoint-related genes, and IC50 of the chemotherapeutic drugs between the low- and high-risk groups. …”

Image 3_Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma.tif by Lichao Cao (276107)

“…Clinicopathologic characteristics were significantly correlated with risk scores. There were significant differences in the proportion of tumor-infiltrating immune cells, the TP53 mutation rate, the expression of immune checkpoint-related genes, and IC50 of the chemotherapeutic drugs between the low- and high-risk groups. …”

Image 1_Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma.tif by Lichao Cao (276107)

“…Clinicopathologic characteristics were significantly correlated with risk scores. There were significant differences in the proportion of tumor-infiltrating immune cells, the TP53 mutation rate, the expression of immune checkpoint-related genes, and IC50 of the chemotherapeutic drugs between the low- and high-risk groups. …”

Table 1_Physical and chemical characteristics of pyrogenic carbon from peatland vegetation fires differ across burn severities.xlsx by Oscar J. Kennedy-Blundell (20506349)

“…</p>Discussion<p>The findings of this study suggest that burn severity is a good predictor of PyC physiochemical characteristics.</p>…”

Aetiology, clinical characteristics, and risk factors influencing mortality in patients with infective endocarditis: a retrospective study by Özkan Vural (21470515)

“…NYHA was an independent predictor of in-hospital mortality.</p> <p>In our study, surgical intervention, i.e. combination therapy, applied after two weeks of antibiotic treatment, was found to be more effective. …”

Supplementary file 1_Tacrolimus exposure during the three-month period following allogeneic stem cell transplantation predicts overall survival.docx by Alzbeta Zavrelova (4885270)

“…The disease risk category (RR 7.11; P < 0.0001), age (RR 2.45; P = 0.0214), and non-myeloablative conditioning (RR 3.39; P = 0.0014) were also significant factors influencing survival in multivariate analysis. …”

Image 1_Application and validation of the machine learning-based multimodal radiomics model for preoperative prediction of lateral lymph node metastasis in papillary thyroid carcin... by Jia-Wei Feng (10754067)

“…Current diagnostic methods show significant limitations, with occult LLNM rates of 41.0%-51.7% requiring secondary surgeries. …”

Association between Lyn tyrosine kinase and renal injury in patients with systemic lupus erythematosus by Jian Sun (27858)

“…ROC curve analysis suggested Lyn as a reliable predictor for SLE, LN, and lupus activity, with histopathological examination revealing that decreased Lyn expression correlated negatively with the renal pathology index (<i>r</i> = −0.8016, <i>p</i> = 0.0016).…”

Data and code from: Limited responses of lizard assemblages to experimental fire regimes in an Australian tropical savanna by angga rachmansah (22007819)

“…The abundance of only one of the five most common species was significantly related to fire frequency. Species richness decreased with increased fire frequency and showed a humped relationship with woody cover. …”

Table 1_Blood urea nitrogen-to-albumin ratio as a new prognostic indicator of 1-year all-cause mortality in patients with IPF.docx by Shaobo Ge (20505809)

“…</p>Conclusion<p>The BAR is a significant predictor of 1-year all-cause mortality of IPF patients, and high BAR values may indicate poor clinical outcomes.…”

<b>Data used for analyses in the manuscript 'Sleep Regularity and Duration are Associated with Depression Severity in a Nationally Representative United States Sample'</b> by Katherine Maki (7445153)

“…Sex was an additional independent predictor, with females exhibiting higher depression scores, and a significant sex × SRI interaction revealed that the inverse relationship between SRI and depressive symptoms was stronger in females than in males. …”

DataSheet1_Using anti-Müllerian hormone to predict premature ovarian insufficiency: a retrospective cross-sectional study.docx by Yuanxin Huang (20286858)

“…At AMH level below 0.5ng/ml,basal FSH increased significantly with age. At FSH <10 IU/L, AMH levels show a trend of rising and then decreasing with age, reaching a peak at approximately 25 years old and gradually decreasing with age. …”

Table 1_Long-term multicomponent exercise enhances functional mobility in early-stage Parkinson’s disease: a 48-month retrospective cohort study.docx by Marcos Moura (22671914)

“…VO₂peak (−1.1 mL·kg⁻¹·min⁻¹; p = 0.142) and muscle mass (−0.7 kg; p = 0.068) showed no significant changes. VO₂peak was the sole independent predictor of functional performance at baseline and follow-up, while muscle mass had limited explanatory value. …”

<b>Mass, Residency Duration, and Previous Experience Shape Post-Escalation but Not Pre-Escalation Outcomes in Experimental Territorial Contests of Atlantic Mudskippers (</b><b><i>P... by Michael Smith (21714377)

“…A binomial generalized linear model with logit link estimated the probability of resident escalation as a time-dependent, predictor-driven decision process, while generalized linear models examined how predictors influenced the resident’s total assessment duration in escalated contests and the post-escalation contest duration. …”

Data Sheet 1_Association of cardiovascular health with COPD (NHANES 2007-2020): mediating potential of lean body mass.docx by Ruoyu Gou (11835929)

“…The lc9 is an suggestive predictor of COPD odds association.</p>Conclusions<p>Higher LC9 scores, particularly when accompanied by increased LBM levels, showed significant associations with reduced COPD risk in cross-sectional analyses.…”

Data Sheet 1_Top motor and non-motor complaints in patients with Parkinson's disease.docx by José Fidel Baizabal-Carvallo (11416508)

“…Cognitive impairment was a significant predictor of non-motor symptoms in their top three tiers [odds ratio [OR]: 3.88 (95% CI: 1.647 to 9.169)]. …”

Data Sheet 1_Reduced hemolytic complement activity in the classical pathway (CH50) is a risk factor for poor clinical outcomes of patients with infections: a retrospective analysis... by Hiroyuki Koami (3878434)

“…</p>Results<p>Of 2,726 patients who met the inclusion criteria, logistic regression models demonstrated that decreased CH50 is a significant predictor of 180-day mortality (OR: 0.98-0.99). …”

Prognostic value of cardiac troponin I in acute ischemic stroke patients treated with reperfusion therapy: a multicenter retrospective cohort study by Andrea Loggini (12077927)

“…Elevated cTnI was not significantly associated with sICH (OR:3.75, 95%CI 0.52–27.21, <i>p</i> = 0.19), but was independently associated with increased in-hospital mortality (OR:1.96, 95%CI 1.62–6.20, <i>p</i> = 0.025) and decreased odds of good functional outcome at 30 days (OR:0.40, 95%CI 0.19–0.84, <i>p</i> = 0.016). …”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…The model used the fully imputed time series of NO₂, SO₂, CO, PM₁₀, and EC as predictor variables. It implicitly learns seasonal patterns from predictor seasonality and was trained using only time periods with measured PM₂.₅ (PM2.5_Source = 1). …”