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In this paper, an automatic assignment tool, called BSS-AutoAssign, for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA) to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY) spectroscopy signals of proteins dissolved in water. Index Terms--Blind source separation, generalized eigenvalue decomposition, matrix pencil, simulated annealing, 2-D NOESY NMR.

Abstract Background As a rule, peptides are more flexible and unstructured than proteins with their substantial stabilizing hydrophobic cores. Nevertheless, a few stably folding peptides have been discovered. This raises the question whether there may be more such peptides that are unknown as yet. These molecules could be helpful in basic research and medicine. Results As a method to explore the space of conformationally stable peptides, we have developed an evolutionary algorithm that allows optimization of sequences with respect to several criteria simultaneously, for instance stability, accessibility of arbitrary parts of the peptide, etc. In a proof-of-concept experiment we have perturbed the sequence of the peptide Villin Headpiece, known to be stable in vitro. Starting from the perturbed sequence we applied our algorithm to optimize peptide stability and accessibility of a loop. Unexpectedly, two clusters of sequences were generated in this way that, according to our criteria, should form structures with higher stability than the wild-type. The structures in one of the clusters possess a fold that markedly differs from the native fold of Villin Headpiece. One of the mutants predicted to be stable was selected for synthesis, its molecular 3D-structure was characterized by nuclear magnetic resonance spectroscopy, and its stability was measured by circular dichroism. Predicted structure and stability were in good agreement with experiment. Eight other sequences and structures, including five with a non-native fold are provided as bona fide predictions. Conclusion The results suggest that much more conformationally stable peptides may exist than are known so far, and that small fold classes could comprise well-separated sub-folds.

Abstract Background We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Döker et al. (1999, BBRC, 257, 348–350). The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS. Results To test this modeling approach it has been used for predicting the structure of the histidine-containing phosphocarrier protein HPr from E. coli and the structure of the human peroxisome proliferator activated receptor γ (Ppar γ). The divergence between the modeled HPr and the previously determined X-ray structure was comparable to the divergence between the X-ray structure and the published NMR structure. The modeled structure of Ppar γ was also very close to the previously solved X-ray structure with an RMSD of 0.262 nm for the backbone atoms. Conclusion In summary, we present a new method for homology modeling capable of producing high-quality structure models. An advantage of the method is that it can be used in combination with incomplete NMR data to obtain reasonable structure models in accordance with the experimental data.

Aims: Metformin has been attributed to cardiovascular protection even in the absence of diabetes. Recent observations suggest that metformin influences the gut microbiome. We aimed to investigate the influence of metformin on the gut microbiota and hypertensive target organ damage in hypertensive rats., Methods: Male double transgenic rats overexpressing the human renin and angiotensinogen genes (dTGR), a model of angiotensin II-dependent hypertension, were treated with metformin (300 mg/kg/day) or vehicle from 4 to 7 weeks of age. We assessed gut microbiome composition and function using shotgun metagenomic sequencing and measured blood pressure via radiotelemetry. Cardiac and renal organ damage and inflammation were evaluated by echocardiography, histology, and flow cytometry., Results: Metformin treatment increased the production of short-chain fatty acids (SCFA) acetate and propionate in feces without altering microbial composition and diversity. It significantly reduced systolic and diastolic blood pressure and improved cardiac function, as measured by end-diastolic volume, E/A, and stroke volume despite increased cardiac hypertrophy. Metformin reduced cardiac inflammation by lowering macrophage infiltration and shifting macrophage subpopulations towards a less inflammatory phenotype. The observed improvements in blood pressure, cardiac function, and inflammation correlated with fecal SCFA levels in dTGR. In vitro, acetate and propionate altered M1-like gene expression in macrophages, reinforcing anti-inflammatory effects. Metformin did not affect hypertensive renal damage or microvascular structure., Conclusion: Metformin modulated the gut microbiome, increased SCFA production, and ameliorated blood pressure and cardiac remodeling in dTGR. Our findings confirm the protective effects of metformin in the absence of diabetes, highlighting SCFA as a potential mediators., (© 2024 The Author(s). Acta Physiologica published by John Wiley & Sons Ltd on behalf of Scandinavian Physiological Society.)

Rationale & Objective: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease., Study Design: Prospective metabolome-wide association study., Setting & Participants: Persons with CKD enrolled in the GCKD (German CKD) study with metabolite measurements, with external validation within the ARIC (Atherosclerosis Risk in Communities) Study., Exposures: 1,513 urine and 1,416 plasma metabolites (Metabolon Inc) measured at study entry using untargeted mass spectrometry., Outcomes: Main end points were kidney failure (KF) and a composite kidney end point (CKE) of KF, estimated glomerular filtration rate<15mL/min/1.73m 2 , or a 40% decrease in estimated glomerular filtration rate. Death from any cause was a secondary end point. After a median of 6.5 years of follow-up, 500 persons had experienced KF, 1,083 had experienced the CKE, and 680 had died., Analytical Approach: Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design with external validation., Results: 5,088 GCKD study participants were included in analyses of urine metabolites, and 5,144 were included in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with the CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (HR, 1.95; 95% CI, 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, the CKE, and death. External validation of the identified associations of metabolites with KF or the CKE revealed directional consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported., Limitations: Use of observational data and semiquantitative metabolite measurements at a single time point., Conclusions: The observed associations between metabolites and KF, the CKE, or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts., Plain-Language Summary: Incomplete understanding of the variability of chronic kidney disease (CKD) progression motivated the search for new biomarkers that would help identify people at increased risk. We explored metabolites in plasma and urine for their association with unfavorable kidney outcomes or death in persons with CKD. Metabolomic analyses revealed 182 metabolites significantly associated with CKD progression or death. Many of these associations confirmed previously reported findings or were validated by analysis in an external study population. Our comprehensive screen of the metabolome serves as a valuable foundation for future investigations into biomarkers associated with CKD progression., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Motivation: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly., Results: We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest., Availability and Implementation: All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire., (© The Author(s) 2023. Published by Oxford University Press.)

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1 H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

Metabolic fingerprinting by mass spectrometry aims at the comprehensive, semiquantitative analysis of metabolites. Isotope dilution, if successfully implemented, may provide a more reliable, relative quantification. Therefore, the 13 C labeled yeast extract of the IROA TruQuant kit was added as an internal standard (IS) to human urine samples measured in full-scan mode on a high-performance liquid chromatography-time-of-flight mass spectrometer (HPLC-TOFMS) system. The isotope ratio approach enabled the analysis of 112 metabolites. The correlation with reference data did not improve significantly using 12 C/ 13 C ratios compared to absolute 12 C peak areas. Moreover, using an intricate 13 C-labeled standard increased the complexity of the mass spectra, which made correct signal annotation more challenging. On the positive side, the ratio approach helps to reduce batch effects, but it does not perform better than computational methods such as the "removebatcheffect" function in the R package Limma.

The isocitrate dehydrogenase ( IDH ) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy ( 1 H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1 H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2-95.1%) and a specificity of 72.7% (95% CI, 57.2-85.0%) could be achieved. We concluded that our 1 H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.

Background: In a previous study, we had investigated the intensive care course of patients with coronavirus disease 2019 (COVID-19) in the first wave in Germany by calculating models for prognosticating in-hospital death with univariable and multivariable regression analysis. This study analyzed if these models were also applicable to patients with COVID-19 in the second wave., Methods: This retrospective cohort study included 98 critical care patients with COVID-19, who had been treated at the University Medical Center Regensburg, Germany, between October 2020 and February 2021. Data collected for each patient included vital signs, dosage of catecholamines, analgosedation, anticoagulation, and antithrombotic medication, diagnostic blood tests, treatment with extracorporeal membrane oxygenation (ECMO), intensive care scores, ventilator therapy, and pulmonary gas exchange. Using these data, expected mortality was calculated by means of the originally developed mathematical models, thereby testing the models for their applicability to patients in the second wave., Results: Mortality in the second-wave cohort did not significantly differ from that in the first-wave cohort (41.8% vs. 32.2%, p = 0.151). As in our previous study, individual parameters such as pH of blood or mean arterial pressure (MAP) differed significantly between survivors and non-survivors. In contrast to our previous study, however, survivors and non-survivors in this study showed significant or even highly significant differences in pulmonary gas exchange and ventilator therapy (e.g. mean and minimum values for oxygen saturation and partial pressure of oxygen, mean values for the fraction of inspired oxygen, positive expiratory pressure, tidal volume, and oxygenation ratio). ECMO therapy was more frequently administered than in the first-wave cohort. Calculations of expected mortality by means of the originally developed univariable and multivariable models showed that the use of simple cut-off values for pH, MAP, troponin, or combinations of these parameters resulted in correctly estimated outcome in approximately 75% of patients without ECMO therapy., Competing Interests: The authors have declared that no competing interests exist.

Background: Mutations in BRCA1 and BRCA2 genes are well-established risk factors of breast and ovarian cancer. In our former study, we observed that approximately 6% of unselected ovarian cancer patients in the region of Podkarpacie (South-East Poland) carry BRCA1 causative founder variants, which is significantly lower than in other regions of Poland. Therefore, it is deeply justified to do research based on the sequencing of whole BRCA1 and BRCA2 genes., Methods: We examined 158 consecutive unselected cases of ovarian cancer patients from the region of Podkarpacie. We performed BRCA1 and BRCA2 genes Next-Generation Sequencing study in all cases., Results: Altogether, in 18 of 158 (11.4%) ovarian cancer patients with BRCA1 or BRCA2 pathogenic mutations were found. BRCA1 pathogenic variants were detected in 11 of the 158 (7.0%) ovarian cancer cases. 10 of 11 (91%) detected BRCA1 mutations were founder mutations, detectable with the standard test used in Poland. BRCA2 pathogenic variants were found in 7 of the 158 (4.4%) cases. No BRCA2 pathogenic variants were founder mutations. The median age of patients at the diagnosis of the 18 hereditary ovarian cancers was 57.5 years., Conclusions: The frequency of BRCA1 or BRCA2 gene mutation carriers among patients with ovarian cancer from the Podkarpacie region is comparable to other regions of Poland. However, a significantly higher percentage of BRCA2 gene mutations was observed, that were not detectable with a standard test for detection of founder mutations. Diagnostics based only on testing the BRCA1/2 Polish founder mutations is characterized by relatively low sensitivity in the case of ovarian cancer patients from South-East Poland and should be supplemented by NGS study, in particular of the BRCA2 gene., (© 2022. The Author(s).)

Due to organ shortage and rising life expectancy the age of organ donors and recipients is increasing. Reliable biomarkers of organ quality that predict successful long-term transplantation outcomes are poorly defined. The aim of this study was the identification of age-related markers of kidney function that might accurately reflect donor organ quality. Histomorphometric, biochemical and molecular parameters were measured in young (3-month-old) and old (24-month-old) male Sprague Dawley rats. In addition to conventional methods, we used urine metabolomics by NMR spectroscopy and gene expression analysis by quantitative RT-PCR to identify markers of ageing relevant to allograft survival. Beside known markers of kidney ageing like albuminuria, changes in the concentration of urine metabolites such as trimethylamine-N-oxide, trigonelline, 2-oxoglutarate, citrate, hippurate, glutamine, acetoacetate, valine and 1-methyl-histidine were identified in association with ageing. In addition, expression of several genes of the toll-like receptor (TLR) pathway, known for their implication in inflammaging, were upregulated in the kidneys of old rats. This study led to the identification of age-related markers of biological allograft age potentially relevant for allograft survival in the future. Among those, urine metabolites and markers of immunity and inflammation, which are highly relevant to immunosuppression in transplant recipients, are promising and deserve further investigation in humans.

Rationale & Objective: Stratification of chronic kidney disease (CKD) patients at risk for progressing to kidney failure requiring kidney replacement therapy (KFRT) is important for clinical decision-making and trial enrollment., Study Design: Four independent prospective observational cohort studies., Setting & Participants: The development cohort comprised 4,915 CKD patients, and 3 independent validation cohorts comprised a total of 3,063. Patients were observed for approximately 5 years., Exposure: 22 demographic, anthropometric, and laboratory variables commonly assessed in CKD patients., Outcome: Progression to KFRT., Analytical Approach: A least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model was fit to select laboratory variables that best identified patients at high risk for KFRT. Model discrimination and calibration were assessed and compared against the 4-variable Tangri (T4) risk equation both in a resampling approach within the development cohort and in the validation cohorts using cause-specific concordance (C) statistics, net reclassification improvement, and calibration graphs., Results: The newly derived 6-variable risk score (Z6) included serum creatinine, albumin, cystatin C, and urea, as well as hemoglobin and the urinary albumin-creatinine ratio. In the the resampling approach, Z6 achieved a median C statistic of 0.909 (95% CI, 0.868-0.937) at 2 years after the baseline visit, whereas the T4 achieved a median C statistic of 0.855 (95% CI, 0.799-0.915). In the 3 independent validation cohorts, the Z6C statistics were 0.894, 0.921, and 0.891, whereas the T4C statistics were 0.882, 0.913, and 0.862., Limitations: The Z6 was both derived and tested only in White European cohorts., Conclusions: A new risk equation based on 6 routinely available laboratory tests facilitates identification of patients with CKD who are at high risk of progressing to KFRT., (Copyright © 2021 National Kidney Foundation, Inc. All rights reserved.)

Large-scale clinical outcome studies demonstrated the efficacy of SGLT2 inhibitors in patients with type II diabetes. Besides their therapeutic efficacy in diabetes, significant renoprotection was observed in non-diabetic patients with chronic kidney disease (CKD), suggesting the existence of glucose-independent beneficial effects of SGLT2 inhibitors. However, the relevant mechanisms by which SGLT2 inhibition delays the progression of renal injury are still largely unknown and speculative. Previous studies showed that SGLT2 inhibitors reduce diabetic hyperfiltration, which is likely a key element in renoprotection. In line with this hypothesis, this study aimed to investigate the nephroprotective effects of the SGLT2 inhibitor empagliflozin (EMPA) in different mouse models with non-diabetic hyperfiltration and progressing CKD to identify the underlying diabetes-independent cellular mechanisms. Non-diabetic hyperfiltration was induced by unilateral nephrectomy (UNx). Since UNx alone does not result in renal damage, renal disease models with varying degrees of glomerular damage and albuminuria were generated by combining UNx with high NaCl diets ± deoxycorticosterone acetate (DOCA) in different mouse strains with and without genetic predisposition for glomerular injury. Renal parameters (GFR, albuminuria, urine volume) were monitored for 4-6 weeks. Application of EMPA via the drinking water resulted in sufficient EMPA plasma concentration and caused glucosuria, diuresis and in some models renal hypertrophy. EMPA had no effect on GFR in untreated wildtype animals, but significantly reduced hyperfiltration after UNx by 36%. In contrast, EMPA did not reduce UNx induced hyperfiltration in any of our kidney disease models, regardless of their degree of glomerular damage caused by DOCA/salt treatment. Consistent with the lack of reduction in glomerular hyperfiltration, EMPA-treated animals developed albuminuria and renal fibrosis to a similar extent as H 2 O control animals. Taken together, the data clearly indicate that blockade of SGLT2 has the potential to reduce non-diabetic hyperfiltration in otherwise untreated mice. However, no effects on hyperfiltration or progression of renal injury were observed in hypervolemic kidney disease models, suggesting that high salt intake and extracellular volume might attenuate the protective effects of SGLT2 blockers., Competing Interests: LM is member of the advisory board of Boehringer Ingelheim and AstraZeneca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tauber, Sinha, Berger, Gronwald, Dettmer, Kuhn, Trum, Maier, Wagner and Schweda.)

Background: Data of critically ill COVID-19 patients are being evaluated worldwide, not only to understand the various aspects of the disease and to refine treatment strategies but also to improve clinical decision-making. For clinical decision-making in particular, prognostic factors of a lethal course of the disease would be highly relevant., Methods: In this retrospective cohort study, we analyzed the first 59 adult critically ill Covid-19 patients treated in one of the intensive care units of the University Medical Center Regensburg, Germany. Using uni- and multivariable regression models, we extracted a set of parameters that allowed for prognosing in-hospital mortality., Results: Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course., Competing Interests: The authors have declared that no competing interests exist.

Background: Gastrointestinal dysfunction is a frequent and disabling manifestation of autoimmune polyendocrine syndrome type 1 (APS-1), a rare monogenic multiorgan autoimmune disease caused by the loss of central AIRE-controlled immune tolerance., Objectives: This study aimed to understand the role of the gut microbiome in APS-1 symptoms and potentially alleviate common gastrointestinal symptoms by probiotic intervention., Methods: This study characterized the fecal microbiomes of 28 patients with APS-1 and searched for associations with gastrointestinal symptoms, circulating anti-cytokine autoantibodies, and tryptophan-related metabolites. Additionally, daily doses of the probiotic Lactobacillus rhamnosus GG were administered for 3 months., Results: Of 581 metagenomic operational taxonomic units (mOTUs) characterized in total, 14 were significantly associated with patients with APS-1 compared with healthy controls, with 6 mOTUs depleted and 8 enriched in patients with APS-1. Four overabundant mOTUs were significantly associated with severity of constipation. Phylogenetically conserved microbial associations with autoantibodies against cytokines were observed. After the 3-month intervention with the probiotic L rhamnosus GG, a subset of gastrointestinal symptoms were alleviated. L rhamnosus GG abundance was increased postintervention and corresponded with decreased abundances of Alistipes onderdonkii and Collinsella aerofaciens, 2 species positively associated with severity of diarrhea in patients with APS-1., Conclusions: The APS-1 microbiome correlates with several APS-1 symptoms, some of which are alleviated after a 3-month L rhamnosus GG intervention. Autoantibodies against cytokines appear to shape the gut microbiome by positively correlating with a taxonomically consistent group of bacteria., (Copyright © 2021. Published by Elsevier Inc.)

Cold Atmospheric Plasma (CAP) is an ionized gas near room temperature. Its anti-tumor effect can be transmitted either by direct treatment or mediated by a plasma-treated solution (PTS), such as treated standard cell culture medium, which contains different amino acids, inorganic salts, vitamins and other substances. Despite extensive research, the active components in PTS and its molecular or cellular mechanisms are not yet fully understood. The purpose of this study was the measurement of the reactive species in PTS and their effect on tumor cells using different plasma modes and treatment durations. The PTS analysis yielded mode- and dose-dependent differences in the production of reactive oxygen and nitrogen species (RONS), and in the decomposition and modification of the amino acids Tyrosine (Tyr) and Tryptophan (Trp). The Trp metabolites Formylkynurenine (FKyn) and Kynurenine (Kyn) were produced in PTS with the 4 kHz (oxygen) mode, inducing apoptosis in Mel Im melanoma cells. Nitrated derivatives of Trp and Tyr were formed in the 8 kHz (nitrogen) mode, elevating the p16 mRNA expression and senescence-associated ß-Galactosidase staining. In conclusion, the plasma mode has a strong impact on the composition of the active components in PTS and affects its anti-tumor mechanism. These findings are of decisive importance for the development of plasma devices and the effectiveness of tumor treatment.