The conjugates of 5'-deoxy-5-fluorocytidine and hydroxycinnamic acids - synthesis, anti-pancreatic cancer activity and molecular docking studies.

New amide conjugates 1-6 of hydroxycinnamic acids (HCA) and 5'-deoxy-5-fluorocytidine (5-dFCR), the prodrug of 5-fluorouracil (5-FU), were synthesized and tested in vitro against pancreatic cancer lines (PDAC). The compounds showed slightly higher efficacy against primary BxPC-3 cells (IC50 values of 14-45 µM) than against metastatic AsPC-1 (IC50 values of 37-133 µM), and similar to that of 5-FU for both PDAC lines. Compound 1, which has a para-(acetyloxy)coumaroyl substituent, was found to be the most potent (IC50 = 14 µM) with a selectivity index of approximately 7 to normal dermal fibroblasts (IC50 = 96 µM). The potential pharmacological profiles were discussed on the basis of the ADME data. Docking to the carboxylesterase CES2 showed that the synthesized compounds have the ability to bind via hydrogen bonding between a specific acetate group of the sugar moiety and Ser228, which belongs to the catalytic triad that causes hydrolysis. Docking to albumin, a major transport protein in the circulatory system, revealed a strong interaction of the conjugates at the binding site which is native to warfarin and responsible for its transport in the body.

The Conjugates of Indolo[2,3-b]quinoline as Anti-Pancreatic Cancer Agents: Design, Synthesis, Molecular Docking and Biological Evaluations.

New amide conjugates of hydroxycinnamic acids (HCAs) and the known antineoplastic 5,11-dimethyl-5H-indolo[2,3-b]quinoline (DiMIQ), an analog of the natural alkaloid neocryptolepine, were synthesized and tested in vitro for anticancer activity. The compound 9-[((2-hydroxy)cinnamoyl)amino]-5,11-dimethyl-5H-indolo[2,3-b]quinoline (2), which contains the ortho-coumaric acid fragment, demonstrated dose-dependent effectiveness against both normal BxPC-3 and metastatic AsPC-1 pancreatic cancer cells. The IC50 values for AsPC-1 and BxPC-3 were 336.5 nM and 347.5 nM, respectively, with a selectivity index of approximately 5 for both pancreatic cancer cells compared to normal dermal fibroblasts. Conjugate 2 did not exhibit any hemolytic activity against human erythrocytes at the tested concentration. Computational studies were performed to predict the pharmacokinetic profile and potential mechanism of action of the synthesized conjugates. These studies focused on the ADME properties of the conjugates and their interactions with DNA, as well as DNA-topoisomerase alpha and beta complexes. All of the conjugates studied showed approximately one order of magnitude stronger binding to DNA compared to the reference DiMIQ, and approximately two orders of magnitude stronger binding to the topoisomerase II-DNA complex compared to DiMIQ. Conjugate 2 was predicted to have the strongest binding to the enzyme-DNA complex, with a Ki value of 2.8 nM.

Advancements in bioinformatics and computational biology: 15th annual Polish Bioinformatic Society Symposium.

The Polish Bioinformatic Society (PTBI) Symposium convenes annually at leading Polish Universities, and in 2023, the Silesian University of Technology hosted participants from all over the world. The 15th PTBI Symposium, spanning a 3-day duration and divided into four scientific sessions, gathered around 100 participants and centered on research related to machine learning in biomedicine, RNA structure algorithms, next-generation sequencing methods, and microbiome analysis but was not limited to only those topics. The meeting also recognized outstanding research conducted by young scientists by awarding the best poster and best talk. Finally, the awards for the best PhD, MSc, and BSc thesis in bioinformatics defended in Poland were given. This report summarizes the key highlights and outcomes of the meeting.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.

Introduction: The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation. Methods: We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs. Results: SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges. Discussion: The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes.

As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ2 test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with η2 or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications.

POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021).

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis.

BACKGROUND: When the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution. METHODS: 20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders. RESULTS: nUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks. CONCLUSIONS: rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.

Early and Midterm Results of Orthotopic Heart Transplantation in Poland (2015-2019).

Orthotopic heart transplantation (OHT) has become one of the most expensive and resource-consuming treatment options for patients with end-stage heart failure. It is therefore useful to review clinical data, such as treatment duration after surgery and midterm follow-up in this group of patients. Contemporary epidemiologic data on early and midterm OHT follow-ups including patient demographics, hospitalization rates and related post-OHT morbidity, and mortality are scarce in Poland. The aim of the study was to determine early survival, hospitalization rates related to OHT and related morbidity, and mortality in Poland in the recent decade.

Synthesis and In Vitro Activity of Novel Melphalan Analogs in Hematological Malignancy Cells.

Despite the continuous developments in pharmacology and the high therapeutic effect of new treatment options for patients with hematological malignancies, these diseases remain a major health issue. Our study aimed to synthesize, analyze in silico, and determine the biological properties of new melphalan derivatives. We obtained three methyl esters of melphalan having in their structures amidine moieties substituted with thiomorpholine (EM-T-MEL), indoline (EM-I-MEL), or 4-(4-morpholinyl) piperidine (EM-MORPIP-MEL). These have not yet been described in the literature. The in vitro anticancer properties of the analogs were determined against THP1, HL60, and RPMI8226 cells. Melphalan derivatives were evaluated for cytotoxicity (resazurin viability assay), genotoxicity (alkaline comet assay), and their ability to induce apoptosis (Hoechst33342/propidium iodide double staining method; phosphatidylserine translocation; and caspase 3/7, 8, and 9 activity measurements). Changes in mitochondrial membrane potential were examined using the specific fluorescence probe JC-1 (5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimidazol carbocyanine). The EM-T-MEL derivative had the highest biological activity, showing higher cytotoxic and genotoxic properties than the parent drug. Moreover, it showed a high ability to induce apoptosis in the tested cancer cells. This compound also had a beneficial effect in peripheral blood mononuclear cells (PBMC). In conclusion, we verified and confirmed the hypothesis that chemical modifications of the melphalan structure improved its anticancer properties. The conducted study allowed the selection of the compound with the highest biological activity and provided a basis for chemical structure-biological activity analyses.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies.

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study.

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33, p < 0.0001), body temperature above 38℃ (OR = 1.67, p < 0.0001), muscle aches (OR = 1.30, p = 0.0242), headache (OR = 1.27, p = 0.0405), cough (OR = 1.26, p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55, p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28, p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72, p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Chemical modification of melphalan as a key to improving treatment of haematological malignancies.

Chemical modification of known, effective drugs is one method to improve chemotherapy. Thus, the object of this study was to generate melphalan derivatives with improved cytotoxic activity in human cancer cells (RPMI8226, HL60 and THP1). Several melphalan derivatives were synthesised, modified in their two important functional groups. Nine analogues were tested, including melphalan compounds modified: only at the amino group, by replacing the amine with an amidine group containing a morpholine ring (MOR-MEL) or with an amidino group and dipropyl chain (DIPR-MEL); only at the carboxyl group to form methyl and ethyl esters of melphalan (EM-MEL, EE-MEL); and in a similar manner at both functional groups (EM-MOR-MEL, EE-MOR-MEL, EM-DIPR-MEL, EE-DIPR-MEL). Melphalan derivatives were evaluated for cytotoxicity (resazurin viability assay), genotoxicity (comet assay) and the ability to induce apoptosis (terminal deoxynucleotidyl transferase dUTP nick end labelling, TUNEL, phosphatidylserine externalisation, chromatin condensation, activity of caspases 3/7, 8 and 9 and intracellular concentration of calcium ions) in comparison with the parent drug. Almost all derivatives, with the exception of MOR-MEL and DIPR-MEL, were found to be more toxic than melphalan in all cell lines evaluated. Treatment of cultures with the derivatives generated a significant higher level of DNA breaks compared to those treated with melphalan, especially after longer incubation times. In addition, all the melphalan derivatives demonstrated a high apoptosis-inducing ability in acute monocytic and promyelocytic leukemia cells. This study showed that the mechanism of action of the tested compounds differed depending on the cell line, and allowed the selection of the most active compounds for further, more detailed investigations.