BEMER therapy radiosensitizes microtumors.

<p>(A) Flow chart of colony formation assay. (B) Basal surviving fraction of BEMER (~13 μT, 8 min, 1 h, 24 h) treated and BEMER sham-treated (sham) microtumors. (C) Clonogenic survival after BEMER therapy (~13 μT, 8 min, 1 h, 24 h) combined with radiotherapy (2 and 6 Gy). All results represent mean ± SD. Student's t-test compares BEMER therapy versus sham samples. n = 3. * P < 0.05; ** P < 0.01.</p

The specific BEMER EMF pattern impacts on cancer cell metabolism.

<p>(A) Pie chart showing the number of detected metabolites categorized by pathways (Σ 225). (B) Heatmap comparing levels of metabolites in BEMER signal treated (~13 μT, 8 min) and BEMER sham-treated (sham) A549 cells. Red and blue indicate up- and downregulation, respectively. Cells were cultured in 3D lrECM for 24 h prior to BEMER treatment. (C) Amount of indicated metabolites in A549 cells without (sham) and with BEMER EMF exposure. (D) Scheme of glycolysis and TCA cycle. Metabolites in blue were downregulated, in red upregulated and in black unaffected upon BEMER therapy compared with sham-treated controls. Metabolites depicted in green were not measured in the metabolome analysis. All results represent mean ± SD. Student's t-test. n = 5. * P < 0.05; ** P < 0.01.</p

BEMER signal intensity determines radiosensitization and DSB numbers.

<p>(A) Flow chart of colony formation assay and foci assay. (B) Clonogenic survival after 6-Gy irradiation combined with BEMER therapy (2.7–35 μT; 8 min) of A549 and UTSCC15 cells. (C) Immunofluorescence images show nuclei with γH2AX/53BP1-positive foci after 6-Gy irradiation with (~13 or ~35 μT; 8 min) and without BEMER therapy in A549 cells. (D) Number of γH2AX/53BP1-positive DSBs 24 h after irradiation in A549 and UTSCC15 cells. BEMER sham-treated (sham), irradiated cells served as control. All results represent mean ± SD. Student's t-test. n = 3. * P < 0.05; ** P < 0.01.</p

Sensitivity to chemotherapy and Cetuximab is not influenced by BEMER therapy.

<p>(A) Flow chart of colony formation assay. Cells were plated in 3D lrECM, treated with respective agents followed by BEMER therapy 23 h later. (B) Basal surviving fraction after Cisplatin (0.1 μM; DMEM as control) treatment and BEMER therapy (~13 μT, 8 min). (C) Basal surviving fraction after Gemcitabine (10 nM; DMEM as control) treatment and BEMER therapy (~13 μT, 8 min). BEMER sham-treated (sham) cells served as control. (D) Basal surviving fraction after Cetuximab (5 μg/ml; IgG as control) treatment and BEMER therapy (~13 μT, 8 min). IgG-treated cells served as control. All results represent mean ± SD. Student's t-test. n = 3. * P < 0.05; ** P < 0.01. n.s., not significant.</p

BEMER therapy-mediated radiosensitization remains unaltered upon chemotherapy and Cetuximab.

<p>(A) Flow chart of colony formation assay. (B) Clonogenic survival after 6-Gy irradiation combined with BEMER therapy (~13 μT, 8 min) and Cisplatin (0.1 μM; DMEM as control). (C) Clonogenic survival after 6-Gy irradiation combined with BEMER therapy (~13 μT, 8 min) and Gemcitabine (10 nM; DMEM as control). Sham-treated (sham) but irradiated cells served as control. (D) Clonogenic survival after 6-Gy irradiation combined with BEMER therapy (~13 μT, 8 min) and Cetuximab (5 μg/ml; IgG as control). IgG-treated, irradiated cells served as control. All results represent mean ± SD. Student's t-test. n = 3. * P < 0.05; ** P < 0.01. n.s., not significant.</p

BEMER device and application.

<p>(A) The electromagnetic field (EMF) with a pulse-duration of 30 ms and a pulse-frequency of 30 Hz was generated by a commercially available control unit B.Box Classic (BEMER AG Int.) with 10 different levels of magnetic field intensity (from 0 μT to 35 μT). (B) The mattress applicator with a flat coil system specifically designed for cell culture. (C) Mattress applicator measurements and scheme of how cell culture plates were placed for BEMER therapy (red rectangles).</p

BEMER therapy mediates radiosensitization of cancer cells.

<p>(A) Phase contrast images and (B) basal surviving fraction of 3D grown colonies of BEMER treated (~13 μT, 8 min, 1 h, 24 h) and BEMER sham-treated (sham) cancer cell lines. (C) Flow chart of colony formation assay. (D) Clonogenic cell survival after BEMER therapy (~13 μT, 8 min, 1 h, 24 h) combined with radiotherapy (2 and 6 Gy). All results represent mean ± SD. Student's t-test. n = 3. * P < 0.05; ** P < 0.01.</p

BEMER therapy-mediated radiosensitization depends on treatment intervals and frequency.

<p>(A) Flow chart of colony formation assay. (B) Clonogenic survival after BEMER therapy (~13 μT, 8 min) combined with 6-Gy irradiation of indicated cell lines. BEMER sham-treated (sham) and irradiated cells served as control. Time intervals of 0, 1, 6, and 24 h between BEMER therapy and radiotherapy were applied. (C) Flow chart of colony formation assay. (D) Clonogenic survival of one time or two time BEMER therapy (~13 μT, 8 min) combined with 6-Gy irradiation of indicated cell lines (BEMER sham-treated (sham), irradiated cells as control). All results represent mean ± SD. Student's t-test. n = 3. * P < 0.05; ** P < 0.01. n.s., not significant.</p

Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field

Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field