Understanding FABP7 binding to fatty acid micelles and membranes.

Fatty acid-binding proteins (FABPs) are chaperones that facilitate the transport of long-chain fatty acids within the cell and can provide cargo-dependent localization to specific cellular compartments. Understanding the nature of this transport is important because lipid signaling functions are associated with metabolic pathways impacting disease pathologies including cancer, autism, and schizophrenia. FABPs often associate with cell membranes to acquire and deliver their bound cargo as part of transport. We focus on brain FABP (FABP7), which demonstrates localization to the cytoplasm and nucleus, influencing transcription and fatty acid metabolism. We use a combined biophysical-computational approach to elucidate the interaction between FABP7 and model membranes. Specifically, we use multiple experiments to demonstrate that FABP7 can bind oleic acid and docosahexaenoic acid micelles. Data from NMR and multiscale molecular dynamics simulations reveal that the interaction with micelles is through FABP7's portal region residues. Simulations suggest that binding to membranes occurs through the same residues as micelles. Simulations also capture binding events where fatty acids dissociate from the membrane and enter FABP7's binding pocket. Overall, our data shed light on the interactions between FABP7 and OA or DHA micelles and provide insight into the transport of long-chain fatty acids.

A combined computational-biophysical approach to understanding fatty acid binding to FABP7.

Members of the fatty acid binding protein (FABP) family function as intracellular transporters of long-chain fatty acids and other hydrophobic molecules to different cellular compartments. Brain FABP (FABP7) exhibits ligand-directed differences in cellular transport. For example, when FABP7 binds to docosahexaenoic acid (DHA), the complex relocates to the nucleus and influences transcriptional activity, whereas FABP7 bound with monosaturated fatty acids remains in the cytosol. Preferential binding of FABP7 to polyunsaturated fatty acids like DHA has been previously observed and is thought to play a role in differential localization. However, we find that at 37°C, FABP7 does not display strong selectivity, suggesting that the conformational ensemble of FABP7 and its perturbation upon binding may be important. We use molecular dynamics simulations, NMR, and a variety of biophysical techniques to better understand the conformational ensemble of FABP7, how it is perturbed by fatty acid binding, and how this may be related to ligand-directed transport. We find that FABP7 has high degree of conformational heterogeneity that is substantially reduced upon ligand binding. We also observe substantial heterogeneity in ligand binding poses, which is consistent with our finding that ligand binding is resistant to mutations in key polar residues in the binding pocket. Our NMR experiments show that DHA binding leads to chemical shift perturbations in residues near the nuclear localization signal, which may point toward a mechanism of differential transport.

An implementation of the Martini coarse-grained force field in OpenMM.

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

Synthetic observations from deep generative models and binary omics data with limited sample size.

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g. as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios (ORs) is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 300 variables, with a tendency of over-estimating ORs when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of ORs. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

Emergency button cannula vs. umbilical catheter as neonatal emergency umbilical vein access - a randomized cross-over pilot study.

OBJECTIVES: Establishing immediate intravenous access to a newborn is challenging even for trained neonatologists in an emergency situation. Correct placement of umbilical catheter or an intraosseous needle needs consistent training. We evaluated the time required to correctly place an emergency umbilical button cannula (EUC) or an umbilical catheter (UC) using the standard intersection (S-EUC or S-UC, respectively) or lateral umbilical cord incision (L-EUC) by untrained medical personnel. METHODS: Single-center cross-over pilot-study using a model with fresh umbilical cords. Video-based teaching of medical students before probands performed all three techniques after assignment to one of three cycles with different sequence, using a single umbilical cord divided in three pieces for each proband. RESULTS: Mean time required to establish L-EUC was 89.3 s, for S-EUC 82.2 s and for S-UC 115.1 s. Both application routes using the EUC were significantly faster than the UC technique. There was no significant difference between both application routes using EUC (p=0.54). CONCLUSIONS: Using an umbilical cannula is faster than an umbilical catheter, using a lateral incision of the umbilical vein is an appropriate alternative.

Development and Interrelation of Pain, Depression, and Anxiety in Music Students: Does Successful Treatment of Pain Have an Impact on Mental Health?

OBJECTIVE: Musculoskeletal pain is a common problem among professional musicians as well as music students. Studies have emphasized the effectiveness of music-specific physiotherapy for affected musicians. This study was designed to evaluate if physiotherapy treatment of pain-affected music students had an impact on pain perception as well as psychological well-being. To explore the possible development of musculoskeletal pain, depression, and anxiety, a second sample of pain-free music students, matched for age and gender, was examined twice at identical time intervals. METHODS: A convenience sample of 31 university music students with moderate to severe musculoskeletal pain and 31 pain-free music students, matched in age and gender, were included in the study. Both groups were examined physically and completed biographical, music-related, and psychological questionnaires. Perceived pain intensity was assessed with a visual-analogue scale (VAS), and depression and anxiety symptoms were assessed with the Beck Depression Inventory II (BDI-II) and the Hospital Anxiety and Depression Scale (HAD). Music students with pain received a series of 12 sessions of musician-specific physiotherapy, while controls waited for the same amount of time for retesting. RESULTS: On the 10-cm VAS, music students with pain reported an average improvement in pain intensity from a baseline of 6.25 (SD 1.95) to 2.7 (2.03) after the intervention, while the controls (music students without pain) did not change. Furthermore, music students with pain indicated higher depression and anxiety scores as compared to the control group before and after therapy. After intervention, music students with pain with higher BDI-II scores demonstrated clinical improvement concerning depression, but no significant improvement in mental health was found in the pain group taken as a whole. CONCLUSION: Physiotherapy was effective in reducing pain symptoms in music students affected by chronic musculoskeletal pain. However, physiotherapy did not improve mental health in pain-affected music students. Additional psychotherapeutic interventions may be needed to support music students with psychological comorbidities such as depression and anxiety.

Deep generative models in DataSHIELD.

BACKGROUND: The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. METHODS: The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package "BoltzmannMachines" from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. RESULTS: We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. CONCLUSIONS: Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

DNA repair enzymes ALKBH2, ALKBH3, and AlkB oxidize 5-methylcytosine to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine in vitro.

5-Methylcytosine (5mC) in DNA CpG islands is an important epigenetic biomarker for mammalian gene regulation. It is oxidized to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) by the ten-eleven translocation (TET) family enzymes, which are α-ketoglutarate (α-KG)/Fe(II)-dependent dioxygenases. In this work, we demonstrate that the epigenetic marker 5mC is modified to 5hmC, 5fC, and 5caC in vitro by another class of α-KG/Fe(II)-dependent proteins-the DNA repair enzymes in the AlkB family, which include ALKBH2, ALKBH3 in huamn and AlkB in Escherichia coli. Theoretical calculations indicate that these enzymes may bind 5mC in the syn-conformation, placing the methyl group comparable to 3-methylcytosine, the prototypic substrate of AlkB. This is the first demonstration of the AlkB proteins to oxidize a methyl group attached to carbon, instead of nitrogen, on a DNA base. These observations suggest a broader role in epigenetics for these DNA repair proteins.

Partitioned learning of deep Boltzmann machines for SNP data.

MOTIVATION: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. RESULTS: After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen the joint distribution of SNPs, followed by training several DBMs on SNP partitions that were identified by the screening. Aggregate features representing SNP patterns and the corresponding SNPs are extracted from the DBMs by a combination of statistical tests and sparse regression. In simulated case-control data, we show how this can uncover complex SNP patterns and augment results from univariate approaches, while maintaining type 1 error control. Time-to-event endpoints are considered in an application with acute myeloid leukemia patients, where SNP patterns are modeled after a pre-screening based on gene expression data. The proposed approach identified three SNPs that seem to jointly influence survival in a validation dataset. This indicates the added value of jointly investigating SNPs compared to standard univariate analyses and makes partitioned learning of DBMs an interesting complementary approach when analyzing SNP data. AVAILABILITY AND IMPLEMENTATION: A Julia package is provided at 'http://github.com/binderh/BoltzmannMachines.jl'. CONTACT: binderh@imbi.uni-freiburg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural explanation for the tunable substrate specificity of an E. coli nucleoside hydrolase: insights from molecular dynamics simulations.

Parasitic protozoa rely on nucleoside hydrolases that play key roles in the purine salvage pathway by catalyzing the hydrolytic cleavage of the N-glycosidic bond that connects nucleobases to ribose sugars. Cytidine-uridine nucleoside hydrolase (CU-NH) is generally specific toward pyrimidine nucleosides; however, previous work has shown that replacing two active site residues with Tyr, specifically the Thr223Tyr and Gln227Tyr mutations, allows CU-NH to process inosine. The current study uses molecular dynamics (MD) simulations to gain atomic-level insight into the activity of wild-type and mutant E. coli CU-NH toward inosine. By examining systems that differ in the identity and protonation states of active site catalytic residues, key enzyme-substrate interactions that dictate the substrate specificity of CU-NH are identified. Regardless of the wild-type or mutant CU-NH considered, our calculations suggest that inosine binding is facilitated by interactions of the ribose moiety with active site residues and Ca2+, and π-interactions between two His residues (His82 and His239) and the nucleobase. However, the lack of observed activity toward inosine for wild-type CU-NH is explained by no residue being correctly aligned to stabilize the departing nucleobase. In contrast, a hydrogen-bonding network between hypoxanthine and a newly identified general acid (Asp15) is present when the two Tyr mutations are engineered into the active site. Investigation of the single CU-NH mutants reveals that this hydrogen-bonding network is only maintained when both Tyr mutations are present due to a π-interaction between the residues. These results rationalize previous experiments that show the single Tyr mutants are unable to efficiently hydrolyze inosine and explain how the Tyr residues work synergistically in the double mutant to stabilize the nucleobase leaving group during hydrolysis. Overall, our simulations provide a structural explanation for the substrate specificity of nucleoside hydrolases, which may be used to rationally develop new treatments for kinetoplastid diseases.

The stereodynamics of macrocyclic succinimide-thioethers.

Maleimide-thiol coupling is a popular bioconjugation strategy, but little is known about the stereoselectivity and the stereodynamics of the succinimide thioether formed in a biopolymer environment. We used thiol 1,4-addition for the macrocyclisation of 5 designed pentapeptides with the ringsize of hexapeptides because they incorporate the succinimide thioether (4-8). Both succinimide diastereomers are observed in the constrained macrocyclic rings in each case. In spite of the low diastereoselectivity of the macrocyclisation reaction, there is a significant influence of the amino acid environment on the epimerization rate of the succinimide. Its half life can be as short as several hours at room temperature when Gly is the amino acid following the succinimide (peptide 8). On the contrary, no epimerization is detectable even after several weeks in the case of d-Phe C-terminal to the succinimide in peptide 4. Already the small selection of examples shows how big the differences in epimerization rates can be and that the local environment has a significant influence. The variation of amino acids in the vicinity of the ligation site points the way towards the synthesis of bioconjugates which are obtained as stable and separable diastereomers.

Quantum Chemical Studies of the Structure and Stability of N-Methylated DNA Nucleobase Dimers: Insights into the Mutagenic Base Pairing of Damaged DNA.

DNA is constantly under attack from exogenous and endogenous sources that modify the chemical structure of the nucleobases. A common type of nucleobase damage is N-methylation, which can result in mutagenesis. Nevertheless, these lesions are often repaired by the DNA repair enzyme AlkB, albeit at varying rates. Herein we use density functional theory (B3LYP-D3(BJ)/6-311++G(2df,2p)//B3LYP/6-31G(d,p)) to comprehensively examine the structural and energetic properties of base pairs between seven nucleobase lesions resulting from N-methylation on the Watson-Crick (WC) binding face and each canonical nucleobase. By characterizing 105 stable nucleobase dimers, we provide fundamental details regarding the preferred lesion base pairings. Specifically, we reveal that the flexibility of the methylamino group resulting from methylation of an exocyclic amino substituent allows the 2MeG, 4MeC, and 6MeA lesions to maintain a preference for canonical WC base pairing, which correlates with the experimentally reported lack of mutagenicity for these damage products. In contrast, calculated distortions in key structural parameters and altered binding energies for base pairs involving adducts formed upon methylation of a ring nitrogen (namely, 1MeG, 3MeT, 1MeA, and 3MeC) help rationalize the associated mutagenicity and repair efficiencies. Most importantly, our work provides molecular-level information about the interactions between N-methylated and canonical nucleobases that is critical for future large-scale modeling of damaged DNA and enzyme-DNA complexes that strive to further uncover the mutagenicity and repair propensities of these detrimental lesions.

Evaluating the Substrate Selectivity of Alkyladenine DNA Glycosylase: The Synergistic Interplay of Active Site Flexibility and Water Reorganization.

Human alkyladenine DNA glycosylase (AAG) functions as part of the base excision repair (BER) pathway by cleaving the N-glycosidic bond that connects nucleobases to the sugar-phosphate backbone in DNA. AAG targets a range of structurally diverse purine lesions using nonspecific DNA-protein π-π interactions. Nevertheless, the enzyme discriminates against the natural purines and is inhibited by pyrimidine lesions. This study uses molecular dynamics simulations and seven different neutral or charged substrates, inhibitors, or canonical purines to probe how the bound nucleotide affects the conformation of the AAG active site, and the role of active site residues in dictating substrate selectivity. The neutral substrates form a common DNA-protein hydrogen bond, which results in a consistent active site conformation that maximizes π-π interactions between the aromatic residues and the nucleobase required for catalysis. Nevertheless, subtle differences in DNA-enzyme contacts for different neutral substrates explain observed differential catalytic efficiencies. In contrast, the exocyclic amino groups of the natural purines clash with active site residues, which leads to catalytically incompetent DNA-enzyme complexes due to significant reorganization of active site water. Specifically, water resides between the A nucleobase and the active site aromatic amino acids required for catalysis, while a shift in the position of the general base (E125) repositions (potentially nucleophilic) water away from G. Despite sharing common amino groups, the methyl substituents in cationic purine lesions (3MeA and 7MeG) exhibit repulsion with active site residues, which repositions the damaged bases in the active site in a manner that promotes their excision. Overall, we provide a structural explanation for the diverse yet discriminatory substrate selectivity of AAG and rationalize key kinetic data available for the enzyme. Specifically, our results highlight the complex interplay of many different DNA-protein interactions used by AAG to facilitate BER, as well as the crucial role of the general base and water (nucleophile) positioning. The insights gained from our work will aid the understanding of the function of other enzymes that use flexible active sites to exhibit diverse substrate specificity.

Hydrolysis of the damaged deoxythymidine glycol nucleoside and comparison to canonical DNA.

Genomic integrity is continually under attack by both endogenous and exogenous sources. One of the most common forms of damage is oxidation of the thymine nucleobase to form (5R,6S)-dihydroxy-5,6-dihydro-thymine (thymine glycol or Tg), which stops DNA polymerases and is thus cytotoxic. Thymine glycol damage is repaired through a variety of mechanisms, including the multi-step base excision repair (BER) pathway. In the first BER step, the glycosidic bond of the dTg nucleotide is hydrolyzed by a DNA glycosylase. In order to understand the catalytic effect of the glycosylases, the corresponding uncatalyzed mechanisms and barriers are required, as well as an appreciation of the relative reactivity of the glycosidic bond with respect to the corresponding canonical nucleoside. To this end, the PCM-B3LYP/6-31+G(d) reaction potential energy surfaces (PES) for deoxythymidine (dT) and dTg hydrolysis are characterized in the present study using solvent-phase optimizations and a model containing three explicit water molecules. The surfaces are comparable to those generated using functionals that account for dispersion interactions (B3LYP-D3 and M06-2X). Mapping the PES as a function of the glycosidic bond length and nucleophile-sugar distance reveals a synchronous S(N)2 mechanism as the lowest energy pathway for damaged dTg hydrolysis, which contrasts the preferred dissociative S(N)1 mechanism isolated for the deglycosylation of natural dT. As proposed for other enzymes, the difference in excision pathway may at least in part help the enzyme selectively target the damaged base and discriminate against the natural counterpart. Interestingly, the barrier to dTg deglycosylation (ΔG(‡) = 138.0 kJ mol(-1)) is much higher than for dT deglycosylation (ΔG(‡) = 112.7 kJ mol(-1)), which supports the stability of this lesion and clarifies the catalytic feat presented to DNA repair enzymes that remove this detrimental damage from the genome. Although nucleotide excision repair (NER) typically targets bulky DNA lesions, the large calculated barrier for dTg deglycosylation rationalizes why the NER mechanism also excises this non-bulky lesion from cellular DNA.

Hybrid computational methods combining experimental information with molecular dynamics.

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

Cardiac Function Modifies the Impact of Heart Base Dose on Survival: A Voxel-Wise Analysis of Patients With Lung Cancer From the PET-Plan Trial.

INTRODUCTION: Heart dose has emerged as an independent predictor of overall survival in patients with NSCLC treated with radiotherapy. Several studies have identified the base of the heart as a region of enhanced dose sensitivity and a potential target for cardiac sparing. We present a dosimetric analysis of overall survival in the multicenter, randomized PET-Plan trial (NCT00697333) and for the first time include left ventricular ejection fraction (EF) at baseline as a metric of cardiac function. METHODS: A total of 205 patients with inoperable stage II or III NSCLC treated with 60 to 72 Gy in 2 Gy fractions were included in this study. A voxel-wise image-based data mining methodology was used to identify anatomical regions where higher dose was significantly associated with worse overall survival. Univariable and multivariable Cox proportional hazards models tested the association of survival with dose to the identified region, established prognostic factors, and baseline cardiac function. RESULTS: A total of 172 patients remained after processing and censoring for follow-up. At 2-years posttreatment, a highly significant region was identified within the base of the heart (p < 0.005), centered on the origin of the left coronary artery and the region of the atrioventricular node. In multivariable analysis, the number of positron emission tomography-positive nodes (p = 0.02, hazard ratio = 1.13, 95% confidence interval: 1.02-1.25) and mean dose to the cardiac subregion (p = 0.02, hazard ratio = 1.11 Gy-1, 95% confidence interval: 1.02-1.21) were significantly associated with overall survival. There was a significant interaction between EF and region dose (p = 0.04) for survival, with contrast plots revealing a larger effect of region dose on survival in patients with lower EF values. CONCLUSIONS: This work validates previous image-based data mining studies by revealing a strong association between dose to the base of the heart and overall survival. For the first time, an interaction between baseline cardiac health and heart base dose was identified, potentially suggesting preexisting cardiac dysfunction exacerbates the impact of heart dose on survival.

Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning.

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 (1H-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 1H-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 1H-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.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

Impact of radiotherapy protocol adherence in NSCLC patients treated with concurrent chemoradiation: RTQA results of the PET-Plan trial.

INTRODUCTION: The success of intensification and personalisation of the curative treatment of non-small cell lung cancer (NSCLC) is strongly associated with the precision in radiotherapy. Here, we evaluate the impact of radiotherapy protocol adherence in a prospective multicentre trial. METHODS: In the open-label, randomised, controlled PET-Plan trial, patients with inoperable NSCLC were randomized at a 1:1 ratio regarding the target volume delineation informed by 1F-FDG PET and CT plus elective nodal irradiation (arm A) or target volumes informed by PET alone (arm B) and received iso-toxically dose-escalated concurrent chemoradiation. The prospectively organised quality assurance program (RTQA) included individual case review by predefined criteria. For evaluation, protocol adherence was scored as per protocol (pP), with minor (miD), intermediate (inD) and major (maD) deviations. In order to exclude biases through patients who discontinued treatment, patients who received ≥60 Gy were additionally analysed. RESULTS: Between 05/2009-11/2016, 205 patients were randomized, 204 patients started treatment according to protocol of which 31 (15%) patients had maD. Patients with maD had an inferior overall survival (OS) (HR 2.9, 95% CI 1.8-4.4, p < 0.0001) and a higher risk of loco-regional progression (HR 5.7, 95% CI 2.7-11.1, p < 0.0001). These results were significant also in the subgroup of patients receiving ≥ 60 Gy. Patients with maD concerning normal tissue delineation and/or dose constraints had a worse OS (p = 0.006) although no higher incidence of grade ≥ 3 toxicities. CONCLUSIONS: Non-adherence to the radiotherapy protocol was associated with an inferior OS and loco-regional control. These results underline the importance of RTQA.

[Blast injury]. / Das Explosionstrauma--Ein seltener, besonders schwieriger Fall.

The blast injury is characterized by 3 different patterns of injury: blast wave, splintered fragments and displacement of victim's body. Severe external and internal hemorrhage, tension pneumothorax and the lethal trios (hypothermia, acidosis, coagulopathy) require a rapid prehospital and inhospital trauma care according to a standardized protocol. The concepts of damage control resuscitation and damage control surgery have proven to be effective and should be integrated into the treatment.