Allergy

Spatial proteomics of tertiary lymphoid structures reveals correlation with immunotherapy response in Merkel cell carcinoma.

BACKGROUND: Tertiary lymphoid structures (TLS) are ectopic lymphoid formations within or around tumours. They are emerging as important predictors of prognosis and response to immune checkpoint inhibitors (ICIs) in various cancers. However, in Merkel cell carcinoma (MCC), the correlation between TLS and ICI response has not yet been evaluated. Furthermore, the criteria for assessing TLS maturity v...

DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images.

Cancer is a complex disease characterized by uncontrolled cell growth and requires an accurate classification for effective treatment. T cell receptors (TCRs), crucial proteins in the immune system, play a pivotal role in antigen recognition. Advancements in sequencing technologies have facilitated the comprehensive profiling of TCR repertoires, uncovering TCRs with potent anticancer activity and ...

A Hybrid Approach for Differentiating Fibrotic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis: Deep Feature Extraction and Machine Learning.

In patients over 60 years of age who are male, non-smokers, and in whom no organic antigen is identified in the history, differentiating fibrotic hype...

From pathogenesis to precision medicine in systemic lupus erythematosus: Emerging biomarkers and targeted interventions.

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease characterized by immune dysregulation, autoantibody production, and chron...

Update on pleural mesothelioma.

PURPOSE OF REVIEW: Pleural mesothelioma remains a universally lethal cancer with a rising global burden. This underscores the need for pulmonologists ...

Recent Advances in Analytical Techniques for Cancer Diagnostics and Therapeutics: Combining State-of-the-Art Technologies for Precision Oncology.

Cancer remains a leading global health challenge, necessitating continuous advancements in diagnostics and therapeutics to enhance patient outcomes. A...

Machine learning-based multi-omic analysis identifies CEP55, DLGAP5, and EZH2 as regulated cell death biomarkers linked to immunotherapy resistance in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) often exhibits limited responsiveness to immune checkpoint inhibitors (ICIs), largely due to an immunosuppressive tumor...

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study.

BACKGROUND: Large language models use machine learning to produce natural language. These models have a range of potential applications in health care...

Decoding immunotherapy response through computational modeling.

Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and t...

Food safety assessment of hemipteran-specific insecticidal protein Tpp78Aa from Bacillus thuringiensis.

BACKGROUND: Hemipteran pests pose a significant threat to global agriculture, creating a continuous need for effective and safe control strategies. Tp...

[Comparative observation of analgesic efficacy between liposomal bupivacaine and "cocktail" therapy following artificial intelligence-assisted direct anterior approach total hip arthroplasty: A prospective randomized controlled study].

OBJECTIVE: To compare the analgesic efficacy and safety of liposomal bupivacaine (LB) versus ropivacaine for surgical incision local anesthesia after ...

STC2-mediated calcium homeostasis dysregulation in the osteosarcoma microenvironment: Insights from single-cell sequencing and machine learning.

BACKGROUND: Osteosarcoma (OS) is an aggressive bone malignancy with a poor prognosis. Dysregulated calcium homeostasis may contribute to OS progressio...

Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.

Accurate neoantigen prediction is central to the design of personalized cancer immunotherapy. The immune recognition of neoantigens is a multi-step pr...

Personalized neoantigen cancer vaccines: Why clinical benefit remains inconsistent.

Personalized cancer vaccines have re-emerged as a promising strategy in precision immunotherapy, driven by advances in tumor sequencing, neoantigen id...

Regulatory B cells and SIVA1-LAG3 immune suppression predict poor prognosis in prostate cancer.

Prostate cancer (PCa) is among the most prevalent malignancies affecting men globally. The immunosuppressive characteristics of the tumor microenviron...

Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.

The existing TNM staging system provides insufficient prognostic information in gastric cancer (GC) patients. This study aims to establish a pathomics...

Efferocytosis-related biomarkers and immune infiltration in pancreatic ductal adenocarcinoma based on single-cell sequencing and machine learning: a comprehensive bioinformatics analysis and experimental validation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and lethal malignancy, with its complex tumor microenvironment (TME) severely...

Targeting cholesterol metabolism: a core regulator of tumor-associated macrophage plasticity and immunotherapy response.

Tumor-associated macrophages (TAMs) are one of the main immunosuppressive components in the tumor microenvironment (TME). Growing evidence indicates t...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions.

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chr...