Latest AI and machine learning research in allergy for healthcare professionals.
Deep learning on physiological time series is interpreted through domain-specific features -- oscillatory rhythms in EEG, morphological complexes in ECG -- yet these signals sit atop a broadband aperiodic 1/f-like envelope that covaries with arousal, age, and pathology. We introduce a spectral audit framework combining aperiodic/periodic decomposition, phase-preserving Fourier interventions, sham ...
Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We...
Background: Adverse drug events (ADEs) are a critical indicator of patient safety but are often documented only in free-text clinical notes. The poten...
Perturbations of genes with functional importance in T cells could be used to change the distribution of CD8 T cell states to enhance anti-tumor funct...
Though currently a minor crop, faba bean is a promising source of plant-based protein as global diets shift towards more plant-based nutrition. To rea...
Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immun...
Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothes...
While tree pollen is a major trigger of allergic respiratory conditions and different taxa exhibit varying allergenic potentials, the lack of high-res...
Prediction of conformational B-cell epitopes is critical for vaccine design, immunotherapy, and antibody engineering. To date, several host-independen...
Introduction Cellular differentiation and lineage commitment are known to be associated with differences in DNA methylation. Leiomyosarcoma (LMS) is a...
Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially...
Effective anti-tumor T cell response depends on both neoantigen quality (non-selfness) and quantity (abundance). However, existing methods for neoanti...
T-cell receptor (TCR) repertoires encode the organization of adaptive immunity and its reshaping by cancer and therapy, but disentangling treatment-as...
The HIV reservoir that establishes early upon infection and persists in tissues remains the primary barrier to a functional cure. While progress has b...
Objectives: Large language models (LLMs) have shown promise in creating clinical codelists for research purposes, a time-consuming task requiring expe...
Tumor-infiltrating lymphocytes are essential for anti-tumor immunity, yet distinguishing tumor-reactive T cells from non-reactive bystander cells rema...
Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with p...
Noncontact exfiltration of electronic screen content poses a security challenge, with side-channel incursions as the principal vector. We introduce an...
Multimodal Large Language Models frequently suffer from inference hallucinations, partially stemming from language priors dominating visual evidence. ...
The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite sub...