A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.

Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets.

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks.

Purpose: This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.

Methods: Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors.

Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records.

Background: Lung cancer remains a significant public health concern, accounting for a considerable number of cancer-related deaths worldwide. Neural networks have emerged as a promising tool that can aid in the diagnosis and treatment of various cancers. Consequently, there has been a growing interest in exploring the potential of artificial intelligence (AI) methods in medicine.

Cytogenetic findings in Polish patients with suspected Fanconi anemia.

Background: The high sensitivity of cells of Fanconi anemia (FA) patients to DNA cross-linking agents (clastogens), such as mitomycin C (MMC), was used as a screening tool in Polish children with clinical suspicion of FA.

Objectives: The aim of the study was to compare chromosome fragility between 3 groups, namely non-FA, possible mosaic FA and FA patients.

Material And Methods: The study included 100 children with hematological manifestations and/or congenital defects characteristic of FA, and 100 healthy controls.

Effects of Tobacco Smoking on the Survivability of Patients with Multiple Cancers and Single Lung Cancer.

Lung cancer is the leading cause of death worldwide among men and women. Tobacco smoking is the number one risk factor for lung cancer. The aim of our study was to evaluate the survivability of patients with single lung cancer in relation to the survival time in patients with multiple neoplasms whose last neoplasm was a lung cancer.

Prediction of semen quality using artificial neural network.

Examination of semen characteristics is routinely performed for fertility status investigation of the male partner of an infertile couple as well as for evaluation of the sperm donor candidate. A useful tool for preliminary assessment of semen characteristics might be an artificial neural network. Thus, the aim of the present study was to construct an artificial neural network, which could be used for predicting the result of semen analysis based on the basic questionnaire data.