Intensivists

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolled oxidative stress and inflammatory responses are central pathological mechanisms. The Sirt1-Nrf2-HO-1 pathway plays a key role in renal antioxidant and anti-inflammatory protection. Although puerarin possesses anti-inflammatory and antioxidant activ...

An Applied Framework of CLSM and Rheology for Controlling Foodborne Biofilms and Their Cleaning Agents and Disinfectants Resistance: A Review.

Biofilms formed by foodborne pathogens pose a significant threat to food safety, as they enhance microbial resistance to disinfectants and environmental stress. Understanding the relationship between their intricate structure and macroscopic mechanical properties is crucial for developing effective control strategies. This review systematically investigates how the combined application of confocal...

Machine learning modeling of vegetation and limited two dimensional urban morphology effects on land surface temperature in Osaka using open data.

Urban heat islands (UHI) significantly elevate land surface temperatures (LST) in high-density subtropical cities like Osaka, Japan, exacerbating ener...

Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.

BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Clinical sentiment analysis of ICU notes: a dataset and a comparison of lexicon and clinical language model methods.

BACKGROUND: General-domain sentiment models have been found ineffective in distinguishing positivity and negativity in Intensive Care Unit (ICU) clini...

Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.

BACKGROUND: Most Artificial Intelligence prognostic models in intensive care are trained and validated on a single source, limiting their reliability ...

Supervised machine learning models for predicting sepsis-associated acute kidney injury in children: a real-world evaluation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) substantially increases mortality. The recent Phoenix criteria have redefined pediatric seps...

Diffusion-Based Generative Model With Scaffold-Hopping Strategy Yields Highly Potent Bioactive Molecules.

As a critical step in drug discovery, lead optimization is a profoundly complex endeavor with a notoriously high failure rate, as it necessitates the ...

Transforming Psoriatic Disease Care Through Innovation and Real-World Data: GRAPPA 2025 Soapbox Highlights.

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2025 annual meeting, held in Bogotá, Colombia, featured soapbox pr...

Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study.

INTRODUCTION: Artificial intelligence (AI) for surgical workflow analysis often fails to generalize because surgical actions lack a standardized, fine...

Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite microstructures using neural operators.

Polymer nanocomposites exhibit highly nonlinear viscoelastic behavior influenced by complex multiscale microstructural interactions between nanofiller...

On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.

During the neonatal period, newborns are more susceptible to developing conditions and diseases due to their fragility of the transition and adaptatio...

Predicting mortality risk in pediatric severe pneumonia using a CNN-BiLSTM model with dynamic clinical indicators.

BACKGROUND: Severe pneumonia is a leading cause of mortality in children under 5 years old, and there is currently a lack of reliable biomarkers for e...

An interpretable multi-task learning model for effluent quality and greenhouse gas emissions prediction in wastewater treatment plants.

Wastewater treatment plants (WWTPs) face growing pressure to comply with regulatory effluent standards while reducing greenhouse gas (GHG) emissions a...

Deep-learning time-series anomaly detection of acute kidney injury from creatinine-eGFR trajectories in the ICU.

Acute kidney injury (AKI) is common in the intensive care unit (ICU), and fixed creatinine thresholds may miss clinically relevant dynamics. We tested...

Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk strati...

Deep learning-based 3D leukocyte differentiation using label-free higher harmonic generation microscopy.

BACKGROUND: Both in clinical practice and translational research, cell differentiation of leukocytes provides important diagnostic information and ins...

ARDSMLpred: A machine learning model for predicting SCAP-associated ARDS based on MIMIC-IV database.

BACKGROUND: Early identification of acute respiratory distress syndrome (ARDS) in severe community-acquired pneumonia (SCAP) are crucial for reducing ...

Editorial for the Special Issue on Harmonization Techniques for MRI.

This editorial introduces the special issue on neuroimaging harmonization and situates its contributions within the broader methodological landscape o...