Sepsis

Association between ePWV and delirium risk in icu sepsis patients: a retrospective analysis based on the MIMIC database and development of a predictive model.

BACKGROUND: Sepsis, frequently complicated by delirium with poor prognosis, is common in the Intensive Care Unit (ICU). Estimated Pulse Wave Velocity (ePWV), a non-invasive arterial stiffness marker, remains unexplored regarding delirium risk in ICU sepsis patients. This study investigated ePWV's association with sepsis-linked delirium and developed a predictive model. METHODS: This retrospective ...

A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.

The rapid emergence of drug-resistant pathogens poses a critical threat to global health. With traditional antibiotics losing efficacy, antimicrobial peptides (AMPs) have gained attention for their unique mechanisms and lower resistance potential. We aimed to accelerate AMP discovery by proposing a closed-loop framework that combines AMP-Hunter (a shared-architecture discriminator for AMP classifi...

Machine Learning-Based Subtype Classification of Sepsis-Associated Acute Kidney Injury and Differential Responses to Renal Replacement Therapy.

BackgroundSepsis-associated acute kidney injury (SA-AKI) is highly heterogeneous with controversial optimal renal replacement therapy (RRT) strategies...

Radiomics and artificial intelligence in pituitary adenomas (PitNETs): from imaging biomarkers to clinical translation.

Pituitary neuroendocrine tumors (PitNETs) are common intracranial neoplasms with heterogeneous hormonal activity, invasiveness, and treatment response...

Brain cognition gaps reveal associations with dopamine and factors related to brain health through artificial intelligence prediction of functional connectome.

A key question in human neuroscience is to understand how individual differences in brain function relate to cognitive differences. However, the optim...

Early Prediction of Sepsis Utilizing Self-Regulated Generative Adversarial Network and Deep Transfer Fuzzy SVM.

Sepsis is a life-threatening condition that can be fatal. Advances in big data analytics and the data-rich environment of intensive care units have en...

Evaluation of a One-Step Serological Test for Lyme Disease.

BACKGROUND: Lyme disease, a vector-borne zoonosis caused by Borrelia species, is classified into early localized and late disseminated infection. Left...

Integrating Target Prediction and Transcriptomic Analysis Identifies the AKR1C2-KRT8 Axis in Rotenone-Induced Dopaminergic Neuron Injury.

Parkinson's disease (PD) is a complex neurodegenerative disorder in which environmental toxins play a critical etiological role. Rotenone, a classical...

Nailfold capillary phenotypes distinguish juvenile myositis subtypes and associate with disease activity.

BACKGROUND: Nailfold capillaroscopy is a non-invasive method to visualize altered microcirculation in pediatric rheumatic disease, with potential to a...

Monitoring and improving the effectiveness of cleaning medical and surgical devices: A practical verification framework.

BACKGROUND: Cleaning is the foundational step in medical device reprocessing; residual soil and biofilm can compromise disinfection and sterilization ...

Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review.

BACKGROUND: Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous...

Development and Validation of an Interpretable Machine Learning Model for Predicting ICU-Acquired Weakness in Postoperative Patients.

BACKGROUND: ICU-acquired weakness (ICU-AW) is a common and debilitating complication among critically ill patients, particularly those undergoing majo...

Comparative outcomes and predictive factors in vacuum, forceps, and sequential vacuum-forceps operative vaginal deliveries.

BACKGROUND: Operative vaginal delivery (OVD) is an important alternative to cesarean section, but the relative outcomes of vacuum, forceps, and sequen...

Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.

BACKGROUND: Emergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)-driv...

From metabolite mapping to precision imaging: advances in in vivo MR spectroscopy of brain tumours.

Magnetic resonance spectroscopy (MRS) provides non-invasive in vivo assessment of brain tumour metabolism and complements conventional magnetic resona...

Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment.

Early sepsis detection is essential for improving outcomes and reducing costs, but traditional rule-based systems have limited accuracy and real-world...

Intrarenal pressure monitoring in retrograde intrarenal surgery for high-infectious risk populations.

PURPOSE: To evaluate the feasibility of real-time intrarenal pressure (IRP) monitoring using the LithoVue™ Elite (LVE) ureteroscope and assess postope...

Real-Time Dynamic Prediction of Mortality and Renal Replacement Therapy After Cardiac Surgery Using a Time-Series Deep Learning Model.

OBJECTIVE: Predicting postoperative deterioration following cardiac surgery remains challenging. Conventional risk scores rely on static variables and...

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.

BACKGROUND: Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify chil...

Unveiling the immuno-inflammatory endotype of sepsis-associated acute kidney injury: a machine learning-based approach.

BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and high-mortality complication in critically ill patients. Current diagnosti...