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. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

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. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components). RESULTS: The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity. CONCLUSIONS: The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method's potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.

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. The present study aimed to evaluate the effectiveness of a neural network in predicting lung cancer recurrence. Methods: The study employed retrospective data from 2,296 medical records of patients diagnosed with lung cancer and admitted to the Warminsko-Mazurskie Center for Lung Diseases in Olsztyn, Poland. The statistical software STATISTICA 7.1, equipped with the Neural Networks module (StatSoft Inc., Tulsa, USA), was utilized to analyze the data. The neural network model was trained using patient information regarding gender, treatment, smoking status, family history, and symptoms of cancer. Results: The study employed a multilayer perceptron neural network with a two-phase learning process. The network demonstrated high predictive ability, as indicated by the percentage of correct classifications, which amounted to 87.5%, 89.1%, and 89.9% for the training, validation, and test sets, respectively. Conclusions: The findings of this study support the potential usefulness of a neural network-based predictive model in assessing the risk of lung cancer recurrence. Further research is warranted to validate these findings and to explore AI's broader implications in cancer diagnosis and treatment.

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. Blood samples obtained from participants were analyzed using an MMC-induced chromosomal breakage test. RESULTS: Patients with clinical suspicion of FA were divided into 3 subgroups based on the MMC test results, namely FA, possible mosaic FA and non-FA. Thirteen out of 100 patients had a true FA cellular phenotype. The mean value of MMC-induced chromosome breaks/cell for FA patients was higher than for non-FA patients (6.67 ±3.92 compared to 0.23 ±0.18). In addition, the percentage of cells with spontaneous aberrations was more than 9 times higher in FA patients than in non-FA patients. CONCLUSIONS: Our results confirmed that the MMC sensitivity test distinguishes between individuals affected by FA, those with possible somatic mosaicism, and patients with bone marrow failure for other reasons, who were classified as non-FA in the first diagnostic step. However, a definitive differential diagnosis requires follow-up mutation testing and chromosome breakage analysis of skin fibroblasts.

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. A retrospective analysis was con-ducted of data from medical histories of patients hospitalized at the Pulmonary Hospital in Olsztyn (Poland) from 2012 to 2017, with a lung cancer diagnosis as the first or subsequent cancer. The total longevity of women with diagnosed multiple cancers was found to be shorter than that of men: 67.60 years (SD: 7.77) and 69.91 years (SD: 7.97), respectively. Among the ex-smokers, the longevity of men (68.93 years) was longer than that of women (66.18 years). Survival time, counted from the diagnosis of both the first and subsequent cancer, was longer among patients with multiple cancers than among patients with single lung cancer (p = 0.000). Women's survivability was worse than men's in the case of multiple cancers and in the group of people who quit smoking (p = 0.037; p = 0.000). To conclude, smoking tobacco affects the survival of patients with lung cancer. Smoking cessation improves overall survival.

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. On the basis of eleven survey questions two models of artificial neural networks to predict semen parameters were developed. The first model aims to predict the overall performance and profile of semen. The second network was developed to predict the concentration of sperm. The network to evaluate sperm concentration proved to be the most efficient. 92.93% of the patients in the learning process were properly qualified for the group with a correct or incorrect result, while the result for the test set was 85.71%. This study suggests that an artificial neural network based on eleven survey questions might be a valuable tool for preliminary evaluation and prediction of the semen profile.