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Structural basis of fungal β-1,3-glucan synthase inhibition by caspofungin
Selection, genetic parameters, and multi-year stability of cacao yield under agroforestry in Rondônia, Brazil
Seeing the Left Main Coronary Artery Clearly — Is IVUS Always Necessary?
Chemicals meant to be eco-friendly accumulate aloft
Coupled effects of surface erosion and magma intrusion on magmatic hazards under a changing climate
Prescription without Precision — Dangers of Dosing on the Basis of Race as Biology
Association between baseline pulse pressure and prognosis in critically ill patients: a retrospective cohort study
Abstract To investigate the association between baseline pulse pressure (PP) and mortality in critically ill patients. This cohort study analyzed 22,035 ICU patients from the MIMIC-IV database. Baseline PP was defined as the first recorded value after admission. Outcomes included in-hospital, 180-day, and 365-day all-cause mortality. Feature importance was assessed using XGBoost. The relationship between PP and mortality was modeled with smooth curve fitting and threshold analysis. Multivariable logistic regression and survival analyses were performed to compare mortality risk across PP groups. Subgroup analyses examined effect modifications. The restricted mean survival time (RMST) analysis validated the robustness of the results. PP holds certain clinical significance in predicting ICU outcomes. A U-shaped relationship was identified, with an inflection point at 49 mmHg (95% CI 46–51). Patients were categorized into three groups: < 46, 46–51, and ≥ 51 mmHg. Using the 46–51 mmHg group as reference, both lower and higher PP groups showed significantly increased risks of in-hospital ( P < 0.05) and long-term mortality ( P < 0.05). Significant interactions were observed in the elderly and septic subgroups. RMST analysis further confirmed the stability of the results. Baseline PP nonlinearly predicts mortality risk in ICU patients, with the optimal range being 46–51 mmHg. This association is stronger in elderly and septic patients.
IVUS-Guided versus Angiography-Guided PCI in Unprotected Left Main Coronary Disease
Feynman solved the ‘restaurant dilemma’ 50 years ago — now a study confirms his mathematics
RANBP3 promotes mitophagy through the CCAR2/SIRT1 pathway to alleviate pyroptosis in macrophages in TBTB
Abstract Mycobacterium tuberculosis infection of the trachea causes tracheobronchial tuberculosis (TBTB), a chronic inflammatory illness, and its pathological features include macrophage pyroptosis. RanBP3, as a potential regulator, participates in cell stress, but its specific mechanism of action in TBTB has not been clarified. The mouse and bone marrow-derived macrophages (BMDMs) models were constructed by infecting with M. smegmatis . RanBP3 protein level and the levels of pyroptosis-related proteins were measured by Western blotting (WB). The levels of inflammatory factors were measured by ELISA. Pearson’s correlation analysis was employed to evaluate their correlation with RanBP3, and the interaction of RanBP3/CCAR2/SIRT1 was verified by Co-immunoprecipitation (Co-IP). The detection of macrophage pyroptosis and mitophagy was done using flow cytometry, and mitophagy was further assessed by transmission electron microscopy. Bacterial survival in BMDMs was evaluated by colony-forming unit assays. Hematoxylin and eosin (H&E) and Masson staining were used to assess lung tissue damage. RanBP3 was down-regulated in TBTB mucosa. At the cellular level, RanBP3 overexpression alleviated BMDM’s pyroptosis by promoting mitophagy and reduced intracellular bacterial survival, whereas RanBP3 knockdown exacerbated pyroptosis and increased bacterial survival. Mechanistically, RanBP3 competitively binds to CCAR2, disrupts the CCAR2-SIRT1 interaction, and promotes SIRT1 release, thereby activating mitophagy. At the animal level, RanBP3 overexpression promoted mitophagy, alleviated macrophage pyroptosis, and alleviated TBTB, but the SIRT1 inhibitor reversed these effects. RanBP3 mediates the release of SIRT1 through the CCAR2-SIRT1 axis, thereby activating mitophagy and inhibiting pyroptosis, providing a new target for TBTB treatment.
Bleeding Risk with Apixaban vs. Rivaroxaban in Acute Venous Thromboembolism
Microstructural remodeling and mechanical enhancement of expansive and clayey soils induced by magnetized water
Subretinal Gene Therapy for X-Linked Retinoschisis
Featurization strategies of supplementary cementitious materials for enhancing interpretability and precision in machine learning based concrete strength prediction
Abstract This study investigates the impact of input variable processing methods for supplementary cementitious materials (SCMs) on the performance of machine learning (ML)-based concrete strength prediction models. SCMs possess unique pozzolanic and latent hydraulic mechanisms. However, it has not been clearly established whether it is more advantageous to treat variables individually to capture SCM characteristics or to aggregate them into a single variable to avoid the curse of dimensionality. This study compared and analyzed the predictive performance between strategies of individualizing versus combining SCM components using 13 databases. Furthermore, SHAP analysis was used to interpret the physical impact of each strategy on the internal decision-making mechanisms of ML models. The results showed that, in most cases, the individualized model recorded superior accuracy compared to the combined model. Particularly in environments where the strength contribution of each SCM component varied in direction, variable separation prevented the information cancel-out effect, allowing the model to capture physical causality more precisely. Conversely, in simple environments, where the influence of a specific variable such as age was overwhelming or the entire SCM exhibited a uniform trend in strength development, combining SCMs as a single variable could be advantageous for stable performance.