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Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.
Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response h…
FULLTEXT: Machine-learning algorithms -
Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning.
Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it remain underexplored. To address this issue, we analyzed the Gene Expression Omnibus (GEO) a…
FULLTEXT: Machine-learning algorithms -
Exploring the key functions of T cells and the regulation of the immune microenvironment in prostate cancer using single-cell RNA sequencing and bulk RNA sequencing.
The incidence of prostate cancer continues to increase, making it the second most common malignant tumor among men worldwide. Immunotherapy has emerged as a key therapeutic strategy for treating tumors. Numerous studies have established tha…
FULLTEXT: Machine-learning algorithms -
Genetic relationships between the gut microbiota and prostate cancer: Mendelian randomization combined with bioinformatics analysis.
[BACKGROUND] Prostate cancer (PCa) is a leading cause of male cancer-related death globally. While the gut microbiota is linked to PCa, its genetic association remains unclear. [METHODS] We screened genetic instruments related to the gut m…
FULLTEXT: Machine-learning algorithms -
Development and validation of machine learning prognostic models for overall survival in non-surgical prostate cancer patients with bone metastases.
[OBJECTIVE] To construct and interpret a machine learning model for predicting overall survival in nonsurgical prostate cancer with bone metastases (PCBM). [METHODS] Data from 3,378 SEER database patients were utilized to develop machine l…
FULLTEXT: Machine-learning algorithms -
Multi-platform analysis of the tumor immune microenvironment associated with breast cancer subtypes.
Breast cancer is genetically and histologically heterogenous, and is influenced by a variety of factors, including the tumor microenvironment (TME). The PAM50 subtypes; Luminal A, Luminal B, Normal-like, Basal-like and Her2-enriched, are as…
FULLTEXT: Machine-learning algorithms -
Locus-specific transcriptional regulation of transposable elements by p53.
The tumor suppressor p53 protects genomic integrity in part by regulating transposable elements (TEs). Studies of p53-TE interactions rely on synthetic DNA and reporter assays, estimating expression only at the family or subfamily level and…
FULLTEXT: Machine-learning algorithms -
Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning.
[OBJECTIVE] This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). [METHODS] Differentially expressed N…
FULLTEXT: Machine-learning algorithms -
A vision-language model-based approach for lung cancer diagnosis using lossless 3D CT images: evaluation of GPT-4.1 and GPT-4o for patient-level malignancy assessment.
[PURPOSE] Large vision-language models (VLMs), such as GPT-4.1 and GPT-4o, have shown strong potential in medical tasks. However, their application in lossless 3D medical image analysis is still underexplored. This study proposes a GPT-base…
FULLTEXT: Machine-learning algorithms -
Predicting lung cancer survival with attention-based CT slices combination.
Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these…
FULLTEXT: Machine-learning algorithms -
Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.
[BACKGROUND] Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But …
FULLTEXT: Machine-learning algorithms -
A data fusion deep learning approach for accurate organelle-based classification of cancer cells.
[PURPOSE] Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preproce…
FULLTEXT: Machine-learning algorithms -
Secondary poor graft function after autologous stem cell transplantation in multiple myeloma: a case-based expert review and successful rescue with secondary autologous stem cell infusion.
[INTRODUCTION] Secondary poor graft function (PGF) after autologous hematopoietic stem cell transplantation (Auto-HSCT) for multiple myeloma (MM) is rare, often delayed in recognition, and lacks standardized salvage algorithms. [AREAS COVE…
FULLTEXT: Machine-learning algorithms -
Comparative efficacy of intralesional therapies for keloid scars: a network meta-analysis.
TL;DRIt is indicated that combination therapies, most notably 5-FU’s+ corticoids’+ YAG:Laser, offer the greatest benefit, and data for novel agents suggest potential, while data for novel agents suggest potential.
FULLTEXT: Machine-learning algorithms -
Artificial intelligence-assisted FTIR spectroscopy for hormone receptor subtyping in formalin-fixed breast Cancer tissues.
[BACKGROUND] Determination of estrogen receptor (ER) and progesterone receptor (PR) status is critical for breast cancer subtyping and guiding endocrine therapy. Although immunohistochemistry (IHC) remains the diagnostic gold standard, it i…
FULLTEXT: Machine-learning algorithms -
Emerging frontiers in cervical cancer diagnostics: Recent innovations in biosensors for HPV detection.
Cervical cancer (CC) is recognized as a significant health concern impacting females worldwide. The pathogenicity of CC is associated with human papillomavirus (HPV). Timely diagnosis, therefore, becomes imperative for reducing mortality an…
FULLTEXT: Machine-learning algorithms -
High confidence Raman spectroscopy of tumor biomarker proteins through experimental and theoretical cross-validation.
Cancer represents a significant challenge to people's health and safety. Tumor biomarker detection plays a vital role in the precise diagnosis of cancer and finds widespread applications in cancer screening and pathological diagnosis. Exist…
FULLTEXT: Machine-learning algorithms -
Smart graphene-enhanced ceramic material refractive index sensor simulation design developed for highly sensitive breast Cancer detection optimized with machine learning.
Based on the surface plasmon resonance (SPR) technique, the proposed biosensor is investigated as an SPR-based sensing platform for detecting breast cancer cells, specifically MCF-7 and MDA-MB-231 cells. Developed biosensor features an octa…
FULLTEXT: Machine-learning algorithms -
An advanced diagnostic framework for discriminating lung cancer tissue subtypes via the synergy of fourier transform infrared spectroscopy and random forest.
Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment an…
FULLTEXT: Machine-learning algorithms -
Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer.
[OBJECTIVE] To develop and validate a robust, multimodal machine learning framework integrating radiomic and deep learning features from multiplex immunohistochemistry (mIHC) images for comprehensive outcome prediction in colorectal cancer …
FULLTEXT: Machine-learning algorithms