Biochemical Approach to Poly(Lactide)-Copper Composite-Impact on Blood Coagulation Processes

The paper presents the investigation of the biological properties of Poly(Lactide)-Copper composite material obtained by sputter deposition of copper onto Poly(lactide) melt-blown nonwoven fabrics. The functionalized composite material was subjected to microbial activity tests against colonies of Gram-positive (Staphylococcus aureus), Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacteria, Chaetomium globosum and Candida albicans fungal mold species and biochemical-hematological tests including the evaluation of the Activated Partial Thromboplastin Time, Prothrombin Time, Thrombin Time and electron microscopy fibrin network imaging. The substantial antimicrobial and antifungal activities of the Poly(Lactide)-Copper composite suggests potential applications as an antibacterial/antifungal material. The unmodified Poly(Lactide) fabric showed accelerated human blood plasma clotting in the intrinsic pathway, while copper plating abolished this effect. Unmodified PLA itself could be used for the preparation of wound dressing materials, accelerating coagulation in the case of hemorrhages, and its modifications with the use of various metals might be applied as new customized materials where blood coagulation process could be well controlled, yielding additional anti-pathogen effects.

Antibacterial and Antifungal Properties of Polyester, Polylactide, and Cotton Nonwovens and Fabrics, by Means of Stable Aqueous Dispersions Containing Copper Silicate and Some Metal Oxides

Literature reviews have described the applications of silver, copper, and zinc ions and metallic particles of Cu, Ti, and Zn oxides, which have been found to be useful antimicrobial reagents for the biofunctionalization of various materials and their surfaces. For this purpose, compositions of water dispersions containing emulsions of synthetic copolymers based on acrylic and vinyl monomers, polysaccharides (hydroxyethyl cellulose and starch), and various additives with wetting and stabilizing properties were used. Many stable water dispersions of different chemical compositions containing bioactive chemical compounds (copper silicate hydrate, titanium dioxide, and zinc oxide (and other auxiliary substances)) were developed. They were used for the preparation of thin hybrid coatings having good antimicrobial properties against Gram-negative bacteria (Escherichia coli), Gram-positive bacteria (Staphylococcus aureus), and yeast fungus (Candida albicans). Polyester (PES) and polylactide (PLA) nonwovens were modified using the dip-coating method, while PES and cotton fabrics were biofunctionalized by means of dip-coating and coating methods. The antimicrobial (antibacterial and antifungal) properties of the textile materials (nonwovens and fabrics) biofunctionalized with the above-mentioned bioactive agents exhibiting antimicrobial properties (CuSiO3, TiO2, ZnO, or ZnO∙SiO2) were strongly dependent on the agents' content in the water dispersions. The PES and PLA nonwovens, modified on the surface with water compositions containing copper silicate hydrate, showed good antibacterial properties against the Gram-negative bacteria Escherichia coli, even at a content of 1 wt.% CuSiO3∙xH2O, and against the Gram-positive bacteria Staphylococcus aureus, at the content of at least 5 wt.% CuSiO3∙xH2O. The bacterial growth reduction factor (R) was greater than 99% for most of the samples tested. Good antifungal properties against the fungus Candida albicans were found for the PES and PLA nonwoven fabrics modified with dispersions containing 5-7 wt.% CuSiO3∙xH2O and 4.2-5.0 wt.% TiO2. The addition of TiO2 led to a significant improvement in the antifungal properties of the PES and PLA nonwovens modified in this way. For the samples of PES WIFP-270 and FS F-5 nonwovens, modified with water dispersions containing 5.0 wt.% CuSiO3∙xH2O and 4.2-5.0 wt.% TiO2, the growth reduction factor for the fungus Candida albicans (R) reached values in the range of 80.9-98.0%. These new biofunctionalized polymeric nonwoven textile materials can find practical applications in the manufacture of filters for hospital air-conditioning systems and for the automotive industry, as well as in air purification devices. Moreover, similar antimicrobial modification of fabrics with the dip-coating or coating methods can be applied, for example, in the fabrication of fungi- and mold-resistant garden furniture.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

827 Streamlining design of safe and effective TCR therapies with AI

Background

Adoptive cell therapies with T lymphocytes expressing engineered T cell receptors (TCRs) are one of the most promising approaches to cancer therapy.1 However, the experimentally driven development of novel TCR therapies is limited by the enormous biological variability of peptide:Human Leukocyte Antigen:TCR (pHLA:TCR) complexes. The in silico methods hold the promise to streamline the discovery of novel TCR therapies by reducing costs and time of laboratory research. In particular, the prediction of TCR binding to a target antigen, as well as the prediction of TCR off-target toxicity2 can provide useful insights supporting the development of safe therapies. We aimed at the development of an experimentally validated AI model of pHLA:TCR binding that will help to prioritize and reduce the number of in vitro assays necessary to discover novel TCRs for cancer therapies.

Methods

The limiting factor of successful pHLA:TCR binding modeling is data availability and completeness of TCR characterization. To address this issue, we are building an oncological pHLA:TCR database with paired alpha and beta chain TCR sequences. We are collecting and sequencing tumor and normal samples from 100 cancer patients, as part of an observational clinical trial. Those data are then screened with the Ardigen's ArdImmune Vax platform3 4 to select immunogenic epitopes. T cells that bind those epitopes are subsequently sorted and used to generate TCR sequencing data at single-cell resolution. We use data-driven and simulation-based models to extract insights about the dynamics of a pHLA:TCR system to predict the binding probability and explain the inference made by the model.

Results

We optimized our data collection pipeline for the cost-efficient acquisition of a large oncological pHLA:TCR dataset. These data will enable us to build efficient models to streamline the development of TCR therapies against cancer.We benchmarked our modeling approach for pHLA:TCR binding against existing solutions5–7 on publicly available data. We also show how focus on model explainability facilitates the detection of model inconsistency of uncertain predictions by expert inspection. Our toxicity assessment solution2 extends the applicability of our system to the prediction of TCR safety profile.

Conclusions

The presented work shows perspectives and limitations of AI-aided TCR therapy development. We present results for our pHLA:TCR binding model, a TCR-toxicity-screening solution, and the study design of our observational clinical trial. Our growing database of pHLA:TCR interactions will enable us to develop highly predictive pHLA:TCR binding models, in particular for oncological targets.

Acknowledgements

We acknowledge funding through the project “Creating an innovative AI-based (Artificial Intelligence) IN SILICO TECHNOLOGY TCRact to launch a NEW SERVICE for designing and optimizing T-cell receptors (TCR) for use in cancer immunotherapies” cofunded by European Regional Development Fund (ERDF) as part of Smart Growth Operational Programme 2014–2020.

References

Farkona S, Diamandis EP, Blasutig IM. Cancer immunotherapy: the beginning of the end of cancer? BMC Med 2016;14:73. PMCID: PMC4858828.

Murcia Pienkowski VA, Mazzocco G, Niemiec I, Sanecka-Duin A, Krol P, Myronov O, Skoczylas P, Kaczmarczyk J, Blum A. Off-target toxicity prediction in cellular cancer immunotherapies [Internet]. Cytotherapy. 2021;S96. Available from: http://dx.doi.org/10.1016/s1465324921004229.

Stepniak P, Mazzocco G, Myronov A, Niemiec I, Gruba K, Skoczylas P, Sanecka-Duin A, Drwal M, Kaczmarczyk J. AI-augmented design of effective therapeutic cancer vaccines and adoptive cell therapies. Journal For Immunotherapy Of Cancer. Bmc Campus, 4 Crinan St, London N1 9xw, England; 2019.

Mazzocco G, Niemiec I, Myronov A, Skoczylas P, Kaczmarczyk J, Sanecka-Duin A, Gruba K, Król P, Drwal M, Szczepanik M, Pyrc K, Stȩpniak P. AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2. Front Genet. 2021;12:602196. PMCID: PMC8027494.

Weber A, Born J, Rodriguez Martínez M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics. 2021;37(Suppl_1):i237–i244. PMCID: PMC8275323.

Springer I, Besser H, Tickotsky-Moskovitz N, Dvorkin S, Louzoun Y. Prediction of specific TCR-peptide binding from large dictionaries of TCR-Peptide Pairs. Front Immunol 2020;11:1803. PMCID: PMC7477042.

Jurtz VI, Jessen LE, Bentzen AK, Jespersen MC, Mahajan S, Vita R, Jensen KK, Marcatili P, Hadrup SR, Peters B, Nielsen M. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks [Internet]. Available from: http://dx.doi.org/10.1101/433706

AI Aided Design of Epitope-Based Vaccine for the Induction of Cellular Immune Responses Against SARS-CoV-2

The heavy burden imposed by the COVID-19 pandemic on our society triggered the race toward the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. (2020) and Iwasaki and Yang (2020). All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

Raman and FTIR spectroscopy in determining the chemical changes in healthy brain tissues and glioblastoma tumor tissues

Glioblastoma, also called glioblastoma multiforme (GBM), is a particularly malignant form of primary brain tumor. This cancer accounts for 12-15% of all brain tumors. Despite the advances in neurosurgery, radio and chemotherapy the average survival rate is only 12.1-16.6 months. This is due not only to the late diagnosis of the disease, but also to ineffective treatment methods which result from the still low knowledge about the causes of glioblastoma development. Therefore, it is very important to look for new diagnostic methods of detection of the smallest features of cancer. Raman and infrared spectroscopy (FTIR) can be such methods. In this paper we discuss the chemical composition of sample glioblastoma brain tissues and marginal brain tissues using these two spectroscopy methods. Raman and FTIR spectra of cancer brain tissues showed that the highest differences in the chemical composition, compared to the control brain tissue, occur in the areas corresponding to lipids, collagen and proteins. Moreover, Raman spectroscopy also showed significant changes in the cancer tissues in the phosphatidylcholine and sphingomyelin. Interestingly, FTIR spectra after Kramers-Kronig transformations showed signals only for three peaks which corresponded to the vibrations of lipid function groups. Adjustment of the Lorenz function for these three peaks showed that only in the case of cancerous tissues the number of matching lines is different, compared to the control and marginal tissues. Therefore, we assume that lipids could be a spectroscopic marker for brain tumor. Furthermore, principal component analysis (PCA) showed that chemical changes seen between cancer and control tissues are significant and it is possible to differentiate the infected tissue from the healthy one. Interestingly, the PCA analysis also showed that adjacent brain tissues have different chemical composition than the control tissues.

Overrepresentation of the COL3A1 AA genotype in Polish skiers with anterior cruciate ligament injury

Although various intrinsic and extrinsic risk factors for anterior cruciate ligament (ACL) rupture have been identified, the exact aetiology of the injury is not yet fully understood. Type III collagen is an important factor in the repair of connective tissue, and certain gene polymorphisms may impair the tensile strength. The aim of this study was to examine the association of the COL3A1 rs1800255 polymorphism with ACL rupture in Polish male recreational skiers. A total of 321 male Polish recreational skiers were recruited for this study; 138 had surgically diagnosed primary ACL ruptures (ACL-injured group) and 183 were apparently healthy male skiers (control group – CON) who had no self-reported history of ligament or tendon injury. Both groups had a comparable level of exposure to ACL injury. Genomic DNA was extracted from the oral epithelial cells. All samples were genotyped on a real-time polymerase chain reaction instrument. The genotype distribution in the ACL-injured group was significantly different than in CON (respectively: AA=10.1 vs 2.2%, AG=22.5 vs 36.1, GG=67.4 vs 61.8%; p=0.0087). The AA vs AG+GG genotype of COL3A1 (odds ratio (OR)=5.05; 95% confidence interval (CI), 1.62-15.71, p=0.003) was significantly overrepresented in the ACL-injured group compared with CON. The frequency of the A allele was higher in the ACL-injured group (21.4%) compared with CON (20.2%), but the difference was not statistically significant (p=0.72). This study revealed an association between the COL3A1 rs1800255 polymorphism and ACL ruptures in Polish skiers.

Priming of seeds with methyl jasmonate induced resistance to hemi-biotroph Fusarium oxysporum f.sp. lycopersici in tomato via 12-oxo-phytodienoic acid, salicylic acid, and flavonol accumulation

Methyl jasmonate (MeJA) was tested by seed treatment for its ability to protect tomato seedlings against fusarium wilt caused by the soil-borne fungal pathogen Fusarium oxysporum f.sp. lycopersici. Isolated from Solanum lycopersicon L. seeds, cv. Beta fungus was identified as F. oxysporum f.sp. lycopersici Race 3 fungus by using phytopathological and molecular methods. MeJA applied at 0.01, 0.1 and 1 mM reduced spore germination and mycelial growth in vitro. Soaking of tomato seeds in MeJA solution at 0.1 mM for 1 h significantly enhanced the resistance level against the tested fungus in tomato seedlings 4 weeks after inoculation. The extracts from leaves of 15-day-old seedlings obtained from previously MeJA soaked seeds had the ability to inhibit in vitro spore germination of tested fungus. In these seedlings a significant increase in the levels phenolic compounds such as salicylic acid (SA), kaempferol and quercetin was observed. Up-regulation of phenylalanine ammonia-lyase (PAL5) and benzoic acid/salicylic acid carboxyl methyltransferase (BSMT) genes and down-regulation of the isochorysmate synthase (ICS) gene in response to exogenous MeJA application indicate that the phenylalanine ammonia-lyase (PAL), not the isochorismate (IC) pathway, is the primary route for SA production in tomato. Moreover, the increased accumulation of the flavonols quercetin and kaempferol appears closely related to the increase of PAL5, chalcone synthase (CHS) and flavonol synthase/flavanone 3-hydroxylase-like (FLS) genes. Elevated levels of salicylic acid in seedlings raised from MeJA-soaked seeds were simultaneously accompanied by a decrease of jasmonic acid, the precursor of MeJA, and an increase of 12-oxo-phytodienoic acid (OPDA), the precursor of jasmonic acid. The present results indicate that the priming of tomato seeds with 0.1mM MeJA before sowing enables the seedlings grown from these seeds to reduce the attack of the soil-borne fungal pathogen F. oxysporum f.sp. lycopersici, so it can be applied in practice.

[Laser's biostimulation in healing or crural ulcerations]

The objective of this paper was to evaluate effect of laser's biostimulation on the process of healing of crural ulcerations. Three comparative groups of patients, A, B and C, were made at random from the patients with venous crural ulcerations. The group A consisted of 17, the group B 15, the group C 17 patients. The patients in all comparative groups were treated pharmacologically and got compress therapy. Ulcerations at patients in group A were additionally irradiated by light of biostimulation's laser (810 nm) in this way that every time ulcerations got dose of energy 4 J/cm2. The patient's in-group B additionally got blind trial (with placebo in the form of quasi-laserotherapy). The evaluated factors were to estimate how laser's biostimulation causes any changes of the size of the ulcers and of the volume of tissue defect. The speed of changes of size and volume of tissue defect per week was calculated. After the treatment there was statistically significant decrease of size of ulcers in all comparative groups while there was no statistically significant difference between the groups observed. After the treatment there was statistically significant decrease of volume of ulcers only in groups A and C but there was no statistically significant difference between the groups observed.