Transplantation

Electrochemical NO-to-NH3 conversion on TM@NiN2 single-atom catalysts: a DFT and machine learning investigation.

Electrocatalytic reduction of nitrogen monoxide (NO) to ammonia (NH3) offers a win-win solution for environmental remediation and chemical production. The key to realizing this technology lies in designing catalysts with superior performance. This study employs a combined density functional theory (DFT) and machine learning (ML) approach to systematically screen the nitric oxide reduction reaction...

Which approach better samples extreme traffic conflicts? Conventional- vs. machine learning-based sampling methods.

Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those sampled extremes. Although the existing body of research has encapsulated sampling methods within a predominant conventional technique, there is no universally accepted practice on...

Development of Raman Spectroscopy and Machine Learning Methods for Protein Aggregate Quantification: Application to BSA in Chromatographic Processes.

Protein aggregation poses a significant risk to biopharmaceutical product quality, as even minor amounts of oligomeric species can compromise efficacy...

Legume Rotations and Conservation Tillage in Synergy: Yield Gains, Carbon Sequestration, and Climate Resilience.

Leguminous crop rotation (LC) and conservation tillage (CT) are nature-based solutions to mitigate climate change. Previous studies have shown signifi...

Halide Electrolytes for All-Solid-State Sodium Batteries: From Fundamental Chemistry to Interface Engineering.

All-solid-state sodium batteries (ASSSBs) stand out as a transformative energy storage technology, combining sodium's natural abundance with enhanced ...

Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segment...

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation.

BACKGROUND: Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, base...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Comprehensive tumour-immune profiling reveals TREM2+ tumour-associated macrophages facilitating lymph node metastasis in head and neck squamous cell carcinoma.

BACKGROUND: Lymph node (LN) metastasis is a well-established independent prognostic factor in head and neck squamous cell carcinoma (HNSCC). Formation...

Effects of ABO-Incompatible Blood Transfusion on Immune Response and Rejection After Organ Transplantation.

BACKGROUND: Peri-operative blood transfusion is common during solid-organ transplantation, yet the impact of ABO-incompatible (ABOi) blood exposure on...

A Scoping Review of the Photographic Assessment of Donor Liver Steatosis in Transplantation Using Artificial Intelligence.

INTRODUCTION: Accurate evaluation of liver steatosis and overall organ quality is critical for optimizing safe organ utilization in liver transplantat...

Machine learning mortality prediction model for cyclosporine therapy in pediatric aplastic anemia.

The outcomes of children with aplastic anemia receiving cyclosporine monotherapy vary significantly in terms of mortality risk; therefore, a prognosti...

Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.

Artificial intelligence (AI) is increasingly transforming health care, particularly in solid organ transplantation, where it addresses complex challen...

You Didn't Think It Would Be That Easy, Did You? Commentary on "Machine Learning Predictions for Assessing Hard-to-Place Deceased Donor Kidneys".

Machine Learning Predictions for Assessing Hard-to-Place Deceased Donor Kidneys.

RATIONALE & OBJECTIVE: Nearly 20% of deceased donor kidneys in the United States are placed "out-of-sequence" (ie, outside of standard allocation rule...

Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.

BACKGROUND: Venous thromboembolism (VTE) is a common and severe complication in intensive care unit (ICU) patients with sepsis. Conventional risk stra...

Group equivariant pyramid network for respiratory motion correction on PET image.

INTRODUCTION: Respiratory-induced artefacts cause blurring in PET images, impairing lesion identification and diagnosis. Existing correction methods e...

The autonomy paradox in AI-generated content adoption: Creative-specific alternative to TAM model in China's micro-short drama industry.

In China's booming micro-short drama industry, Artificial Intelligence Generated Content (AIGC) presents creators with an 'autonomy paradox': improvin...

Guided electron flow in anthraquinone-methoxy donor-acceptor1-acceptor2 covalent triazine frameworks enabling superior selective uranium capture.

Rational design and synthesis of stable and efficient photocatalysts for selective U(VI) capture in water remains a great challenge due to the complic...

Microvascular inflammation in the kidney transplant, beyond acute antibody-mediated rejection.

PURPOSE OF REVIEW: Microvascular inflammation (MVI) is a central feature of allograft rejection, traditionally associated with antibody-mediated rejec...