Chemotherapy

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet th...

A comparative analysis of saponin-enriched fraction from (Moench) Garcke, (Gaertn) and (Santapau and Fernandes): an hemolytic and cytotoxicity evaluation.

To explore the newer saponin resources, toxicity of saponin-enriched fraction (SEF) extracted from (SV) was evaluated for first time and compared with toxicity of SEF extracted from (SM) and (CV). All extracted SEF from diverse resources were characterized by immersing TLC plates in 0.5% RBC suspension method, by ethanol: sulfuric acid method and by estimating hRst values. Each extracted SEF c...

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to...

Chemical Degradation of Intravenous Chemotherapy Agents and Opioids by a Novel Instrument.

To assess chemical degradation of various liquid chemotherapy and opioid drugs in the novel RxDestruct™ instrument. Intravenous (IV) drug solutions ...

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular target...

A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

BACKGROUND: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be...

Cannabis use is associated with a small increase in the risk of postoperative nausea and vomiting: a retrospective machine-learning causal analysis.

BACKGROUND: Cannabis legalization may contribute to an increased frequency of chronic use among patients presenting for surgery. At present, it is unk...

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to bett...

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has re...

Revealing cytotoxic substructures in molecules using deep learning.

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of hig...

Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

BACKGROUND: Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatien...

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the ha...

Outpatient Robot-Assisted Radical Prostatectomy: Are Patients Ready for Same-Day Discharge?

Several case series have demonstrated the safety and feasibility of outpatient robot-assisted radical prostatectomy (RARP) in well-selected patients;...

Retrospective Cohort Analysis from a High-Volume Center of Prognostic Factors Affecting Biochemical Relapse in Patients with Encapsulated, Margin-Negative, Isolated Seminal Vesicle Invasion After Robot-Assisted Laparoscopic Prostatectomy: A Novel Study.

Specimen pathology findings collectively impact the long-term outcomes of robot-assisted laparoscopic prostatectomy. Since seminal vesicle invasion (...

Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.

BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head ...

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of c...

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) ...

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

The aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV ...

Machine learning to predict early recurrence after oesophageal cancer surgery.

BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadj...

Xuetonglactones A-F: Highly Oxidized Lanostane and Cycloartane Triterpenoids From Roxb. Craib.

Xuetonglactones A-F (-), six unreported highly oxidized lanostane- and cycloartane-type triterpenoids along with 22 known scaffolds (-) were isolated ...