Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Meeting the future through bio-behavioral healthcare sciences; Innovations for Healthcare Science

The call for the establishment of an endowed chair in innovative care was motivated by a need to address a multitude of challenges, including the aging population with increasing chronic diseases and multiple comorbidities; continuing breakthroughs in biomedical and medtech research which change care strategies and needs; and the increasing provision of care outside hospital environments. Meeting these challenges calls for investment in healthcare science research, education, and innovation. To meet these challenges, Investor AB donated SEK 10 million in 2013 to the establishment of an endowed Chair in Innovative Care at Karolinska Institutet. This competitive position, which integrates health and social care research, was awarded to Professor Carol Tishelman in October 2014 and is based at the Department of Learning, Informatics, Management and Ethics (LIME) where she leads the Innovative Care research group (ki.se/en/lime/innovative-care) with Assoc. Prof Lars E. Eriksson. Tishelman has a joint position with Karolinska University Hospital, Center for Innovation, as University nurse with responsibility for strategic healthcare innovation

Mesenchymal stem cell-based therapy for ischemic stroke

Ischemic stroke represents a major, worldwide health burden with increasing incidence. Patients affected by ischemic strokes currently have few clinically approved treatment options available. Most currently approved treatments for ischemic stroke have narrow therapeutic windows, severely limiting the number of patients able to be treated. Mesenchymal stem cells represent a promising novel treatment for ischemic stroke. Numerous studies have demonstrated that mesenchymal stem cells functionally improve outcomes in rodent models of ischemic stroke. Recent studies have also shown that exosomes secreted by mesenchymal stem cells mediate much of this effect. In the present review, we summarize the current literature on the use of mesenchymal stem cells to treat ischemic stroke. Further studies investigating the mechanisms underlying mesenchymal stem cells tissue healing effects are warranted and would be of benefit to the field

DEqMS : A Method for Accurate Variance Estimation in Differential Protein Expression Analysis

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.Peer reviewe

PTEN and DNA-PK determine sensitivity and recovery in response to WEE1 inhibition in human breast cancer

Inhibition of WEE1 kinase by AZD1775 has shown promising results in clinical cancer trials, but markers predicting AZD1775 response are lacking. Here we analysed AZD1775 response in a panel of human breast cancer (BC) cell lines by global proteome/transcriptome profiling and identified two groups of basal-like BC (BLBCs): ‘PTEN low’ BLBCs were highly sensitive to AZD1775 and failed to recover following removal of AZD1775, while ‘PTEN high’ BLBCs recovered. AZD1775 induced phosphorylation of DNA-PK, protecting cells from replication-associated DNA damage and promoting cellular recovery. Deletion of DNA-PK or PTEN, or inhibition of DNA-PK sensitized recovering BLBCs to AZD1775 by abrogating replication arrest, allowing replication despite DNA damage. This was linked to reduced CHK1 activation, increased cyclin E levels and apoptosis. In conclusion, we identified PTEN and DNA-PK as essential regulators of replication checkpoint arrest in response to AZD1775 and defined PTEN as a promising biomarker for efficient WEE1 cancer therapy

Crystalline and oxide phases revealed and formed on InSb(111)B

Oxidation treatment creating a well-ordered crystalline structure has been shown to provide a major improvement for III–V semiconductor/oxide interfaces in electronics. We present this treatment’s effects on InSb(111)B surface and its electronic properties with scanning tunneling microscopy and spectroscopy. Possibility to oxidize (111)B surface with parameters similar to the ones used for (100) surface is found, indicating a generality of the crystalline oxidation among different crystal planes, crucial for utilization in nanotechnology. The outcome is strongly dependent on surface conditions and remarkably, the (111) plane can oxidize without changes in surface lattice symmetry, or alternatively, resulting in a complex, semicommensurate quasicrystal-like structure. The findings are of major significance for passivation via oxide termination for nano-structured III–V/oxide devices containing several crystal plane surfaces. As a proof-of-principle, we present a procedure where InSb(111)B surface is cleaned by simple HCl-etching, transferred via air, and post-annealed and oxidized in ultrahigh vacuum

Cell cycle profiling reveals protein oscillation, phosphorylation, and localization dynamics

The cell cycle is a highly conserved process involving the coordinated separation of a single cell into two daughter cells. To relate transcriptional regulation across the cell cycle with oscillatory changes in protein abundance and activity, we carried out a proteome- and phospho-proteome-wide mass spectrometry profiling. We compared protein dynamics with gene transcription, revealing many transcriptionally regulated G2 mRNAs that only produce a protein shift after mitosis. Integration of CRISPR/Cas9 survivability studies further highlighted proteins essential for cell viability. Analyzing the dynamics of phosphorylation events and protein solubility dynamics over the cell cycle, we characterize predicted phospho-peptide motif distributions and predict cell cycle-dependent translocating proteins, as exemplified by the S-adenosylmethionine synthase MAT2A. Our study implicates this enzyme in translocating to the nucleus after the G1/S-checkpoint, which enables epigenetic histone methylation maintenance during DNA replication. Taken together, this data set provides a unique integrated resource with novel insights on cell cycle dynamics

PARP-1 dependent recruitment of the amyotrophic lateral sclerosis-associated protein FUS/TLS to sites of oxidative DNA damage

Amyotrophic lateral sclerosis (ALS) is associated with progressive degeneration of motor neurons. Several of the genes associated with this disease encode proteins involved in RNA processing, including fused-in-sarcoma/translocated-in-sarcoma (FUS/TLS). FUS is a member of the heterogeneous nuclear ribonucleoprotein (hnRNP) family of proteins that bind thousands of pre-mRNAs and can regulate their splicing. Here, we have examined the possibility that FUS is also a component of the cellular response to DNA damage. We show that both GFP-tagged and endogenous FUS re-localize to sites of oxidative DNA damage induced by UVA laser, and that FUS recruitment is greatly reduced or ablated by an inhibitor of poly (ADP-ribose) polymerase activity. Consistent with this, we show that recombinant FUS binds directly to poly (ADP-ribose) in vitro, and that both GFP-tagged and endogenous FUS fail to accumulate at sites of UVA laser induced damage in cells lacking poly (ADP-ribose) polymerase-1. Finally, we show that GFP-FUS(R521G), harbouring a mutation that is associated with ALS, exhibits reduced ability to accumulate at sites of UVA laser-induced DNA damage. Together, these data suggest that FUS is a component of the cellular response to DNA damage, and that defects in this response may contribute to ALS

Heterogeneity and interplay of the extracellular vesicle small RNA transcriptome and proteome

Extracellular vesicles (EVs) mediate cell-to-cell communication by delivering or displaying macromolecules to their recipient cells. While certain broad-spectrum EV effects reflect their protein cargo composition, others have been attributed to individual EV-loaded molecules such as specific miRNAs. In this work, we have investigated the contents of vesicular cargo using small RNA sequencing of cells and EVs from HEK293T, RD4, C2C12, Neuro2a and C17.2. The majority of RNA content in EVs (49-96%) corresponded to rRNA-, coding-and tRNA fragments, corroborating with our proteomic analysis of HEK293T and C2C12 EVs which showed an enrichment of ribosome and translation-related proteins. On the other hand, the overall proportion of vesicular small RNA was relatively low and variable (2-39%) and mostly comprised of miRNAs and sequences mapping to piRNA loci. Importantly, this is one of the few studies, which systematically links vesicular RNA and protein cargo of vesicles. Our data is particularly useful for future work in unravelling the biological mechanisms underlying vesicular RNA and protein sorting and serves as an important guide in developing EVs as carriers for RNA therapeutics.Peer reviewe

Imaging empty states on the Ge(100) surface at 12 K

Our understanding of bias-dependent scanning-tunneling-microscopy (STM) images is complicated not only by the multiplicity of the surface electronic structure, but also the manifold tunneling effects in probing semiconductor surfaces having directional dangling- and covalent-bond orbitals. Here we present a refined interpretation of empty-state STM images from the model semiconductor surface, Ge(100), on the basis of measurements at low temperature (12 K) combined with density-functional-theory calculations. In the lower-bias regime (<= 1.6 V), the electron tunneling is found to occur predominantly in antibonding dangling-bond or/and dimer-bond states (pi*(1)pi*(2) and sigma*) of Ge(100) at the surface-parallel wave vector k(parallel to) = 0, leading to the tunneling current maxima located directly on the dimer rows. At higher biases (e.g., at 2 V), the current maxima are shifted to the position in the troughs between the dimer rows, because the tunneling occurs efficiently in the pi*(2) states at k(parallel to )not equal 0 associated with the dimer-up atoms of two adjacent dimer rows, i.e., because of increased sideways tunneling. Thus, the empty-state STM images of Ge(100), albeit strongly bias-dependent, reflect the dimer arrangement rather than the backbonds and surface resonances at all experimental conditions used. The results are also discussed in comparison with the counterpart system of Si(100)