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Brain metastases are a common finding upon initial diagnosis of otherwise locally limited non-small cell lung cancer. We present a retrospective case series describing three cases of patients with symptomatic, synchronous brain metastases and resectable lung tumors. The patients received local ablative treatment of the brain metastases followed by neoadjuvant immunochemotherapy with pemetrexed, cisplatin, and pembrolizumab. Afterwards, resection of the pulmonary lesion with curative intent was performed. One patient showed progressive disease 12 months after initial diagnosis, and passed away 31 months after initial diagnosis. Two of the patients are still alive and maintain a good quality of life with a progression-free survival and overall survival of 28 and 35 months, respectively, illustrating the potential of novel combinatorial treatment approaches.
Virus-infected cells are eliminated by cytotoxic T lymphocytes, which recognize viral epitopes displayed on major histocompatibility complex class I molecules at the cell surface. Herpesviruses have evolved sophisticated strategies to escape this immune surveillance. During the lytic phase of EBV infection, the viral factor BNLF2a interferes with antigen processing by preventing peptide loading of major histocompatibility complex class I molecules. Here we reveal details of the inhibition mechanism of this EBV protein. We demonstrate that BNLF2a acts as a tail-anchored protein, exploiting the mammalian Asna-1/WRB (Get3/Get1) machinery for posttranslational insertion into the endoplasmic reticulum membrane, where it subsequently blocks antigen translocation by the transporter associated with antigen processing (TAP). BNLF2a binds directly to the core TAP complex arresting the ATP-binding cassette transporter in a transport-incompetent conformation. The inhibition mechanism of EBV BNLF2a is distinct and mutually exclusive of other viral TAP inhibitors.
The demand to develop convergent technology platforms, such as bio-functionalized medical devices, is rapidly increasing. However, the loss of biological function of the effector molecules during sterilization represents a significant and general problem. Therefore, we have developed and characterized a nano-coating (NC) formulation capable of maintaining the functionality of proteins on biological-device combination products. As a proof of concept, the NC preserved the structural and functional integrity of an otherwise highly fragile antibody immobilized on polyurethane during deleterious sterilizing irradiation (≥ 25 kGy). The NC procedure enables straight-forward terminal sterilization of bio-functionalized materials while preserving optimal conditioning of the bioactive surface.
We report a measurement of the observed cross sections of e+ e− → J/ψX based on 3.21 fb − 1 of data accumulated at energies from 3.645 to 3.891 GeV with the BESIII detector operated at the BEPCII collider. In analysis of the cross sections, we measured the decay branching fractions of B(ψ(3686) → J/ψX) = (64.4 ± 0.6 ± 1.6)% and B(ψ(3770) → J/ψX) = (0.5 ± 0.2 ± 0.1)% for the first time. The energy-dependent line shape of these cross sections cannot be well described by two Breit-Wigner (BW) amplitudes of the expected decays ψ (3686) → J/ψX and ψ(3770) → J/ψX. Instead, it can be better described with one more BW amplitude of the decay R(3760)→ J/ψX. Under this assumption, we extracted the R (3760) mass M R (3760 ) = 3766.2 ± 3.8 ± 0.4 MeV/c2, total width Γ tot R ( 3760 ) = 22.2 ± 5.9 ± 1.4 MeV, and product of leptonic width and decay branching fraction
ΓeeR(3760) B[R(3760) → J/ψX] = (79.4 ± 85.5 ± 11.7) eV. The significance of the R(3760) is 5.3σ. The first uncertainties of these measured quantities are from fits to the cross sections and second systematic.
We demonstrate that strangeness separates in the Gibbs-phase coexistence between a baryon-rich quark-gluon plasma and hadron matter, even at T=0. For finite temperatures this is due to the associated production of kaons (containing s¯ quarks) in the hadron phase while s quarks remain in the deconfined phase. The s-s¯ separation results in a strong enhancement of the s-quark abundance in the quark phase. This mechanism is further supported by cooling and net strangeness enrichment due to the prefreezeout evaporation of pions and K+, K0, which carry away entropy and anti- strangeness from the system. Metastable droplets (i.e., stable as far as weak interactions are not regarded) of strange-quark matter (‘‘strangelets’’) can thus be formed during the phase transition. Such cool, compact, long-lived clusters could be experimentally observed by their unusually small Z/A ratio (≤0.1–0.3). Even if the strange-quark-matter phase is not stable under strong interactions, it should be observable by the delayed correlated emission of several hyperons. This would serve as a unique signature for the transient formation of a quark-gluon plasma.
We demonstrate the occurrence of canonical suppression associated with the conservation of an U(1)-charge in current transport models. For this study a pion gas is simulated within two different transport approaches by incorporating inelastic and volume-limited collisions pi pi leftrightarrow K bar-K for the production of kaon pairs. Both descriptions can dynamically account for the suppression in the yields of rare strange particles in a limited box, being in full accordance with a canonical statistical description.
We present a mechanism for the separation of strangeness from antistrangeness in the deconfinement transition. For a net strangeness of zero in the total system, the population of s quarks is greatly enriched in the quark-gluon plasma, while the s¯ quarks drift into the hadronic phase. This separation could result in ‘‘strangelet’’ formation, i.e., metastable blobs of strange-quark matter, which could serve as a unique signature for quark-gluon plasma formation in heavy-ion collisions. PACS: 25.70.Np, 12.38.Mh
Using data samples collected with the BESIII detector operating at the BEPCII storage ring at center-of-mass energies from 4.178 to 4.600 GeV, we study the process eþe− → π0Xð3872Þγ and search for Zcð4020Þ0 → Xð3872Þγ. We find no significant signal and set upper limits on σðeþe− → π0Xð3872ÞγÞ · BðXð3872Þ → πþπ−J=ψÞ and σðeþe− → π0Zcð4020Þ0Þ · BðZcð4020Þ0 → Xð3872ÞγÞ · BðXð3872Þ → πþπ−J=ψÞ for each energy point at 90% confidence level, which is of the order of several tenths pb.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.