Cellular lactate is a key cellular metabolite and marker of anaerobic glycolysis. Cellular lactate uptake, release, production from glucose and glycogen, and interconversion with pyruvate are important determinants of cellular energy. It is known that lactate is present in the spectrum of neoplasms and low malignancy (without necrotic lesions). Also, the appearance of lactate signals is associated with anaerobic glucose, mitochondrial dysfunction, and other inflammatory responses. The aim of this study was the detection of lactate in cell cultures with the use of proton magnetic resonance (¹H MRS) and a 1.5 Tesla clinical apparatus (MR OPTIMA 360), characterized as a medium-field system. In this study, selected metabolites, together with cellular lactate, were identified with the use of an appropriate protocol and management algorithm. This paper describes the results obtained for cancer cell cultures. This medium-field system has proven the possibility of detecting small molecules, such as lactate, with clinical instruments. ¹H MRS performed using clinical MR apparatus is a useful tool for clinical analysis.

Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query “neural network in medicine” exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.

Background/Objectives: Non-muscle-invasive bladder cancer (NMIBC) accounts for the majority of newly diagnosed bladder cancers and is characterized by a high recurrence rate despite standard treatments. Photodynamic therapy (PDT) using 5-aminolevulinic acid (5-ALA) offers a promising alternative; however, objective methods for monitoring therapeutic response are needed. Quantitative magnetic resonance imaging (MRI), particularly T1 and T2 relaxation mapping, may provide non-invasive biomarkers of tissue response to PDT. Methods: In this ex vivo study, 50 samples obtained during transurethral electro-resection of bladder tumors were analyzed using a 1.5 Tesla MRI scanner. Twenty-five healthy control tissues (n = 25) served as the control group. Twenty-five NMIBC tissues were analyzed twice: before and after 5-ALA-PDT. T1 and T2 relaxation times were measured, and regions of interest (ROIs) were manually segmented to obtain quantitative values. Statistical analyses were performed to assess the significance of differences observed between study groups. Results: T1 relaxation times significantly differed between groups: 1351.7 ± 271.1 ms in healthy tissue, 727.7 ± 145.0 ms in untreated NMIBC, and 368.9 ± 65.2 ms in NMIBC after PDT (p < 0.0001). T2 values were 93.5 ± 20.3 ms (healthy), 78.5 ± 20.4 ms (NMIBC), and 55.7 ± 6.1 ms (NMIBC_PDT), with significant reduction after PDT (p < 0.01). Conclusions: Quantitative T1 and T2 mapping via MRI is a sensitive and non-invasive method for detecting structural and biochemical changes in bladder tissue following 5-ALA-PDT. These relaxation times may serve as reliable biomarkers for therapeutic response, facilitating in real-time monitoring and personalized treatment planning for NMIBC. Further in vivo studies are warranted to validate these findings and integrate them into clinical practice.

The aim of the study was to investigate the effect of Trastuzumab on the MCF-7 and CRL-2314 breast cancer cell lines. Additionally, an attempt was made to optimize magnetic resonance spectroscopy (MRS) for cell culture studies, with particular emphasis on the impact of treatment with Trastuzumab. The research materials included MCF-7 and CRL-2314 breast cancer cell lines. The study examined the response of these cell lines to treatment with Trastuzumab. The clinical magnetic resonance imaging (MRI) system, OPTIMA MR360 manufactured by GEMS, with a magnetic field induction of 1.5 T, was used. Due to the nature of the tested objects, their size and shape, it was necessary to design and manufacture additional receiving coils. They were used to image the tested cell cultures and record the spectroscopic signal. The spectra obtained by MRS were confirmed by NMR using a 300 MHz NMR Fourier 300 with the TopSpin 3.1 system from Bruker. The designed receiving coils allowed for conducting experiments with the cell lines in a satisfactory manner. These tests would not be possible using factory-delivered coils due to their parameters and the size of the test objects, whose volume did not exceed 1 mL. MRS studies revealed an increase in the metabolite at 1.9 ppm, which indicates the induction of histone acetylation. Changes in histone acetylation play a very important role in both cell development and differentiation processes. The use of Trastuzumab therapy in breast cancer cells increases the levels of acetylated histones. MRS studies and spectra obtained from the 300 MHz NMR system are consistent with the specificity inherent in both systems.