Allergy & Immunology

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting a convenient and effective strategy for cancer therapy. However, therapies based on conventional photosensitizers often suffer from limitations such as a single photosensitizing mechanism, restricted tumor penetration and retention, and the requirement for multiple irradiations, which significantly ...

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develop and validate a deep learning model using a novel voxel-level radiomics approach to predict pCR based on preoperative CT images.

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, of...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening pati...

Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems.

Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in ass...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditiona...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who...

Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.

BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individua...

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for re...

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major...

Factors associated with the development of severe asthma: A nationwide study (FINASTHMA).

BACKGROUND: Severe asthma presents a major challenge to health care and negatively affects the quality of life of patients. Understanding the factors ...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth fa...

Machine learning enhanced immunologic risk assessments for solid organ transplantation.

The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML)....

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by t...

GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models.

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. However, their acquisition and optimization present sig...

Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data.

Predictive biomarker identification in cancer treatment has traditionally relied on pre-defined analyses, limiting discoveries to expected biomarkers ...

Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from populatio...

Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic age...

Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds.

Skin sensitization, or allergic contact dermatitis, represents a critical end point in toxicity assessment, with profound implications for drug safety...

Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investig...