Chemotherapy

Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques.

In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The extracts were tested for phenolic content, antioxidant potential, acetylcholinesterase and butyrylcholinesterase inhibitory activities and antiproliferative effects against A549 lung cancer cell...

Addressing Hemolysis-Induced Loss of Sensitivity in Lateral Flow Assays of Blood Samples with Platinum-Coated Gold Nanoparticles and Machine Learning.

Gold nanoparticles (GNPs), which appear red, are widely used as labels in lateral flow assays (LFAs) for visual detection. However, in blood-derived samples, hemolysis─caused by the rupture of red blood cells and the release of hemoglobin─creates a red-colored background that obscures the test line, significantly raising the limit of detection (LOD) and reducing the diagnostic sensitivity of LFAs....

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction model...

Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning.

BACKGROUND: This study aimed to evaluate the impact of nipple-sparing mastectomy (NSM) and modified radical mastectomy (MRM) on individual survival ou...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not...

Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Mo...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annua...

Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.

BACKGROUND: Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuv...

Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.

BACKGROUND AND PURPOSE: Accurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional h...

Machine learning-based prediction reveals kinase MAP4K4 regulates neutrophil differentiation through phosphorylating apoptosis-related proteins.

Neutrophils, an essential innate immune cell type with a short lifespan, rely on continuous replenishment from bone marrow (BM) precursors. Although i...

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...

Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study des...

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.

RATIONALE: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for ...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole s...

Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.

No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT) in locally advanced rectal cancer (LARC) treated ...

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, aiming to enhance their quality of life. A non-rand...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy...

Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.

OBJECTIVE:  Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, often resulting in suboptimal chemotherapy outcome...

Enhanced hair regrowth with five monthly sessions of minoxidil-dutasteride-copper peptides tattooing for androgenetic alopecia assessed by artificial intelligence and blinded evaluators.

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical ...