Surgery

Prompt-Driven Target Identification: A Multi-Omics and Network Biology Case Study of PARP1 Using Swalife PromptStudio

Artificial intelligence–assisted scientific prompting is reshaping how biological targets can be rapidly identified and contextualized. In this work, we present the Swalife PromptStudio – Target Identification workflow and illustrate its application to poly(ADP-ribose) polymerase-1 (PARP1), a central regulator of DNA repair and genome integrity. Using structured prompts, we systematically explored...

Comparative Computational Modeling of Approach–Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference

Pavlovian “approach or avoid” impulses are critical behavioral biases that, in excess, are linked to multiple psychiatric conditions. To investigate how such biases contribute to suicidal thoughts and behaviors, we analyzed data from two clinical populations completing an aversive Go/NoGo task. This task disentangles motor action (Go or NoGo) from outcome valence (escape from, or avoidance of, an ...

Enhancing fMRI decoded neurofeedback with co-adaptive training: simulation and proof-of-principle evidence

A significant challenge for neurofeedback training research and related clinical applications, is participants’ difficulty in learning to induce speci...

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate ...

Integrating Data Across Oscillatory Power Bands Predicts the Seizure Onset Zone in Focal Epilepsies

Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) remains challenging. Although diverse methods...

Automated Specimen Triage for Dark Taxa: Deep Learning Enables Orientation, Sex Identification, and Anatomical Segmentation from Robotic Imaging

Robotic specimen processing is transforming biodiversity discovery by replacing labor-intensive handling with scalable systems that can simultaneously...

Transforming Esogastric Cancer Surgery Integrating SpiderMass Mass Spectrometry with Clinical and Microbiome Data for Margin Delineation and Prognosis

Esophageal-gastric cancers (EC) represent a significant global health concern, with esophageal cancer ranking seventh in terms of incidence and mortal...

Dynamic and task-dependent decoding of the human attentional spotlight from MEG

Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive elec...

Deep Learning Bridges Histology and Transcriptomics to Predict Molecular Subtypes and Outcomes in Muscle-Invasive Bladder Cancer

Muscle-Invasive Bladder Cancer (MIBC) is a heterogeneous disease with distinct molecular subtypes influencing prognosis and therapeutic response. Howe...

Competition between memories for reactivation as a mechanism for long-delay credit assignment

Animals can associate events with their outcomes, even if there is a long delay between the two. For example, in conditioned taste aversion, animals g...

Explainable Machine Learning for Preoperative Relapse Prediction in Molecularly Stratified Endometrial Cancer: A Single-Center Finnish Cohort Study

Relapse risk in endometrial carcinoma (EC) is strongly influenced by molecular subtype, yet current WHO/ESGO classifications rely on postoperative dat...

Closing the Sim-to-Real Gap: An End-to-End Robotic Ultrasound System Leveraging In Vivo Reinforcement Learning and 3D-Prior Guided Hybrid Control

Abdominal ultrasound is a crucial first-line diagnostic tool, yet its efficacy is inherently constrained by a strong dependency on operator skill, lea...

Integration of artificial intelligence and high-content screening enabled identification of drugs for long-term treatment of cerebral cavernous malformation disease

Adults and children with cerebral cavernous malformations (CCMs) are at risk of experiencing lifelong complications such as hemorrhagic strokes, neuro...

Heart rate fragmentation improves general anesthesia state classification using machine learning

Accurate assessment of consciousness during general anesthesia is crucial for optimizing anesthetic dosage and patient safety. Current electroencephal...

GLUCOSE: A Distributional Reinforcement Learning Model for Optimal Glucose Control After Cardiac Surgery

This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement lear...

Quantitative AI based on evolutionary computation and information theory combined with mathematical functions yields algorithmic RNA biomarkers from a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient responses to anti-TNF treatment, to enable precision medicine

The routine derivation of novel biomarkers from therapeutic clinical trials to accurately predict individual patient’s responses, would be a significa...

Deep Learning–Based Early Detection of Major Adverse Cerebral Injuries in Cardiothoracic and Vascular Surgery

Despite advances in central nervous system (CNS)-protective anesthetic and surgical strategies, perioperative stroke remains a significant concern in ...

End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning

Foundation models (FMs) show promise in medical AI by learning flexible features from large datasets, potentially surpassing handcrafted radiomics. Ou...

A pragmatic randomized controlled trial of artificial intelligence (AI)-based predictive analytics monitoring for early detection of clinical deterioration

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

Systemic Metabolic Alterations after Aneurysmal Subarachnoid Hemorrhage: A Plasma Metabolomics Approach

Aneurysmal subarachnoid hemorrhage (aSAH) causes systemic changes that contribute to delayed cerebral ischemia (DCI) and morbidity. Circulating metabo...