Allergy & Immunology

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2,517 patients treated in the ICU of a tertiary hospital in China from January 2019 to December 2023 were included, of whom 455 (18.1%) developed an HAI. Data on 32 potential risk factors for infection were consi...

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy.

This narrative review examines the promising potential of integrating artificial intelligence (AI) with CRISPR-Cas9 genome editing to advance CAR T-cell therapy. AI algorithms offer unparalleled precision in identifying genetic targets, essential for enhancing the therapeutic efficacy of CAR T-cell treatments. This precision is critical for eliminating negative regulatory elements that undermine t...

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy...

Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot.

In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and rob...

Integrated epigenomic exposure signature discovery.

The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagn...

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive...

Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

BACKGROUND: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmenta...

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.

INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent...

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

BACKGROUND: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor ...

Application of artificial intelligence in rheumatic disease: a bibliometric analysis.

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the predicti...

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificia...

Using machine learning to dissect host kinases required for Leishmania internalization and development.

The Leishmania life cycle alternates between promastigotes, found in the sandfly, and amastigotes, found in mammals. When an infected sandfly bites a ...

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma.

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-e...

Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques.

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by t...

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning.

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-relat...

Use of explainable AI on slit-lamp images of anterior surface of eyes to diagnose allergic conjunctival diseases.

BACKGROUND: Artificial intelligence (AI) is a promising new technology that has the potential of diagnosing allergic conjunctival diseases (ACDs). How...

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset.

The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. ...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of myc...

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune...

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities u...