Family Wellbeing and Sexual Health of Patients Receiving Treatment for Prostate Cancer.

Purpose: Prostate cancer and its treatment may affect patients' sexual function and social wellbeing. This study investigated the relationship between social/family wellbeing and sexual health in patients with prostate cancer. Additionally, the moderating effect of clinical characteristics on this relationship was also explored. Patients and Methods: This is a descriptive correlational study using baseline data of a longitudinal study enrolling 137 patients with prostate cancer. Sexual Function (SF) and Sexual Function Distress (SFD) data were collected using the Symptom Index questionnaire. Demographic data were obtained during study intake and clinical data were obtained from chart review. Bivariate correlation determined the correlations among continuous demographic/clinical data, social/family wellbeing, and sexual health. Moderated regression analysis determined the moderating effects of clinical characteristics on the relationship of social/family wellbeing and sexual health. Results: Moderate positive correlation was found between social/family wellbeing and SF, whereas a weak negative correlation was noted between social/family wellbeing and SFD. Depression was significantly correlated with social/family wellbeing and SFD. Both sexual health domains were significantly correlated with Gleason score. A significant difference was noted in the social/family wellbeing and both SF and SFD in participants receiving androgen deprivation therapy (ADT) compared to those not receiving ADT. Concomitant ADT use was the only clinical characteristic found to be a significant moderator of the relationship between social/family wellbeing and SFD, but none of the clinical characteristics was found to have a moderating effect on the relationship of social/family wellbeing and SF. Among patients who were not receiving ADT, high social/family wellbeing was associated with low SFD. Patients who were receiving ADT reported slightly higher SFD despite having higher social/family wellbeing. Conclusion: Ensuring sexual health in patients with prostate cancer requires a comprehensive approach to address factors contributing to sexual health such as side effects of treatment and family wellbeing.

Making Every Single Puff Count-Simple and Sensitive E-Cigarette Aerosol Sampling for GCxIMS and GC-MS Analysis.

The analysis of the aerosol from tobaccoless electronic cigarettes (e-cigarettes) is an important part of understanding their impact on human health, yet sampling aerosol from e-cigarettes is still considered a challenge. It lacks a standard method for research and quality control and there are a variety of methods. However, few are simple and inexpensive, and none have been suggested for the use with gas chromatography coupled ion mobility spectrometry (GCxIMS). This work presents and evaluates such a setup made from standard lab equipment to quickly collect a quantitative sample from the aerosol of a single puff (5 s totaling 125 mL). The aerosol condensates directly in the cooled headspace (HS) vial, which is analyzed in the HS-GCxIMS or mass spectrometer (HS-GC-MS). The combined use of GC-MS and GCxIMS allows the simple and sensitive identification of unknown substances in complex mixtures and the identification of degradation products in the aerosols. A calibration of 26 flavor compounds (0.2-20 µg/g) was created using single puffs of a spiked, flavorless commercial refill solution and 2-alkanones as internal standards. This sensitive but easily reproducible setup enables a wide range of further investigations, even for labs that were previously unable to afford it.

Processes in DNA damage response from a whole-cell multi-omics perspective.

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Quantitation of Flavor Compounds in Refill Solutions for Electronic Cigarettes Using HS-GCxIMS and Internal Standards.

New regulations on the use of flavor compounds in tobaccoless electronic cigarettes require comprehensive analyses. Gas chromatography coupled ion mobility spectrometry is on the rise as an analytical technique for analyzing volatile organic compounds as it combines sensitivity, selectivity, and easy usage with a full-range screening. A current challenge is the quantitative GCxIMS-analysis. Non-linear calibration methods are predominantly used. This work presents a new calibration method using linearization and its corresponding fit based on the relation between the reactant and analyte ions from the chemical ionization. The analysis of e-liquids is used to compare the presented calibration with an established method based on a non-linear Boltzmann fit. Since e-liquids contain matrix compounds that have been shown to influence the analyte signals, the use of internal standards is introduced to reduce these effects in GCxIMS-analysis directly. Different matrix mixtures were evaluated in the matrix-matched calibration to improve the quantitation further. The system's detection and quantitation limits were determined using a separate linear calibration. A matrix-matched calibration series of 29 volatile compounds with 12 levels were used to determine the concentration of these substances in a spiked, flavorless e-liquid and a banana-flavored e-liquid, validating the quality of the different calibrations.

Differentiation of Monofloral Honey Using Volatile Organic Compounds by HS-GCxIMS.

Honey is a natural product and can be described by its botanical origin, determined by the plants from which the bees collect nectar. It significantly influences the taste of honey and is often used as a quality criterion. Unfortunately, this opens up the possibility of food fraud. Currently, various methods are used to check the authenticity of monofloral honey. The laborious, manual melissopalynology is considered an essential tool in the verification process. In this work, the volatile organic compounds obtained from the headspace of honey are used to prove their authenticity. The headspace of 58 honey samples was analyzed using a commercial easy-to-use gas chromatography-coupled ion mobility spectrometer with a headspace sampler (HS-GCxIMS). The honey samples were successfully differentiated by their six different botanical origins using specific markers with principal component analysis in combination with linear discriminant analysis. In addition, 15 honey-typical compounds were identified using measurements of reference compounds. Taking a previously published strategy, retention times of marker compounds were correlated with GC-coupled mass spectrometry (GC-MS) measurements to assist in the identification process.

Microbench: automated metadata management for systems biology benchmarking and reproducibility in Python.

MOTIVATION: Computational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility. RESULTS: Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes. AVAILABILITY AND IMPLEMENTATION: Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Programmatic modeling for biological systems.

Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with community standards used for interchange. Models undergo steady state or dynamic analysis, which can include simulation and calibration within a single application, or transfer across various tools. Here, we describe a novel programmatic modeling paradigm, whereby modeling is augmented with software engineering best practices. We focus on Python - a popular programming language with a large scientific package ecosystem. Models can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators, while still being extensible and exportable to standardized formats for use with external tools if desired. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.

Family history of cancer in first degree relatives and risk of cancer of unknown primary.

OBJECTIVE: Cancer of Unknown Primary (CUP) refers to the presence of metastatic lesions, with no identifiable primary site during the patient's lifetime. Poor survival and lack of available treatment highlight the need to identify potential CUP risk factors. We investigated whether a family history of cancer is associated with increased CUP risk. METHODS: We performed a case cohort analysis using data from the Netherlands Cohort Study, which included a total of 963 CUP cases and 4,288 subcohort members. A Cox Proportional Hazards Regression was used to compare CUP risk in participants who reported to have a family member with cancer to those who did not, whilst adjusting for confounders. RESULTS: In general, we observed no increased CUP risk in those who reported a family history of cancer. CUP risk appeared slightly increased in those who reported cancer in a sibling (HR: 1.16, 95% CI: 0.97-1.38), especially in those with a sister with cancer compared with those without (HR: 1.23, 95% CI: 0.99-1.53), although these findings are not statistically significant. CONCLUSION: Having a family history of cancer is not an independent risk factor of CUP.

Selective Inhibition of JAK1 Primes STAT5-Driven Human Leukemia Cells for ATRA-Induced Differentiation.

BACKGROUND: All-trans retinoic acid (ATRA), a derivate of vitamin A, has been successfully used as a therapy to induce differentiation in M3 acute promyelocytic leukemia (APML), and has led to marked improvement in outcomes. Previously, attempts to use ATRA in non-APML in the clinic, however, have been underwhelming, likely due to persistent signaling through other oncogenic drivers. Dysregulated JAK/STAT signaling is known to drive several hematologic malignancies, and targeting JAK1 and JAK2 with the JAK1/JAK2 inhibitor ruxolitinib has led to improvement in survival in primary myelofibrosis and alleviation of vasomotor symptoms and splenomegaly in polycythemia vera and myelofibrosis. OBJECTIVE: While dose-dependent anemia and thrombocytopenia limit the use of JAK2 inhibition, selectively targeting JAK1 has been explored as a means to suppress inflammation and STAT-associated pathologies related to neoplastogenesis. The objective of this study is to employ JAK1 inhibition (JAK1i) in the presence of ATRA as a potential therapy in non-M3 acute myeloid leukemia (AML). METHODS: Efficacy of JAK1i using INCB52793 was assessed by changes in cell cycle and apoptosis in treated AML cell lines. Transcriptomic and proteomic analysis evaluated effects of JAK1i. Synergy between JAK1i+ ATRA was assessed in cell lines in vitro while efficacy in vivo was assessed by tumor reduction in MV-4-11 cell line-derived xenografts. RESULTS: Here we describe novel synergistic activity between JAK1i inhibition and ATRA in non-M3 leukemia. Transcriptomic and proteomic analysis confirmed structural and functional changes related to maturation while in vivo combinatory studies revealed significant decreases in leukemic expansion. CONCLUSIONS: JAK1i+ ATRA lead to decreases in cell cycle followed by myeloid differentiation and cell death in human leukemias. These findings highlight potential uses of ATRA-based differentiation therapy of non-M3 human leukemia.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery.

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

Thunor: visualization and analysis of high-throughput dose-response datasets.

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose-response curves, and derived dose-response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net.

Strategy for the identification of flavor compounds in e-liquids by correlating the analysis of GCxIMS and GC-MS.

This work presents a strategy to correlate the results from gas chromatography coupled ion mobility spectrometry (GCxIMS) and mass spectrometry (GC-MS) to enable a simpler and cheaper analysis of flavor compounds in e-liquids. The use of the retention index for GCxIMS measurements was validated for its application to correlate results with GC-MS data. The easy detection of the GCxIMS for substances at concentrations as low as 1 µg/L can therefore be combined with the identification power of the MS. The use of the MS' mass signals and wide-spread availability of mass spectra libraries reduces the effort necessary to choose the correct reference standards for the identification of unknown substances. Between both detectors, correlating of the retention time indices was achieved for ± 1%. 2-Alkanones were used as an alternative reference point for the IMS and the well-established alkanes for the MS. The application on flavor compounds in e-liquids shows equal or better results than those presented for more complex, hardware-based correlations like line splitting. Additionally, the inverted reduced mobility combined with the retention index of a non-polar column enables simple extrapolation for the confirmation of expected substances as well as the use in a transferable database. For the first time, this comprehensive application allows an extensive, simplified, and cheap identification of flavor compounds in e-liquids by GCxIMS and GC-MS.

SynLeGG: analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationships.

Achilles' heel relationships arise when the status of one gene exposes a cell's vulnerability to perturbation of a second gene, such as chemical inhibition, providing therapeutic opportunities for precision oncology. SynLeGG (www.overton-lab.uk/synlegg) identifies and visualizes mutually exclusive loss signatures in 'omics data to enable discovery of genetic dependency relationships (GDRs) across 783 cancer cell lines and 30 tissues. While there is significant focus on genetic approaches, transcriptome data has advantages for investigation of GDRs and remains relatively underexplored. SynLeGG depends upon the MultiSEp algorithm for unsupervised assignment of cell lines into gene expression clusters, which provide the basis for analysis of CRISPR scores and mutational status in order to propose candidate GDRs. Benchmarking against SynLethDB demonstrates favourable performance for MultiSEp against competing approaches, finding significantly higher area under the Receiver Operator Characteristic curve and between 2.8-fold to 8.5-fold greater coverage. In addition to pan-cancer analysis, SynLeGG offers investigation of tissue-specific GDRs and recovers established relationships, including synthetic lethality for SMARCA2 with SMARCA4. Proteomics, Gene Ontology, protein-protein interactions and paralogue information are provided to assist interpretation and candidate drug target prioritization. SynLeGG predictions are significantly enriched in dependencies validated by a recently published CRISPR screen.

Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling.

Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted "neutral" (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington's epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.

Analysis of e-liquids for electronic cigarettes using GC-IMS/MS with headspace sampling.

Electronic cigarettes (e-cigarettes) are continuously increasing in popularity due to being considered healthier compared with traditional cigarettes. Based on the European directive 2014/40/EU [1] the German legislation restricts the usage of potential harmful substances in these tobacco related products [2,3]. The aim of this work was to establish a method for the detection and quantification of selected substances covered by the above regulation. For this purpose, one-of-a-kind gas chromatograph with ion mobility spectrometer (GC-IMS) was used. Instrument was parallelly coupled with conventional chromatograph equipped with quadrupole mass spectrometer (GC-MS). Headspace (HS) was used as a sample preparation technique. During initial tests both systems were correlated by using a mixture of simple carbonyl compounds. The identification of the selected analytes was performed by mass spectrometry and calibration curves for the quantification were recorded. For all tested substances the limit of detection (LOD) and limit of quantification (LOQ) were determined. The LOD ranges are from 8 to 70 µg/L, the LOQ are from 25 to 200 µg/L. For testing the usability of the developed method 20 samples of commercially available refills for e-cigarettes (e-liquids), produced in Germany and Poland, were analyzed. Substances listed in the directive were found in all samples. In two of them an according to the regulations forbidden substance (estragole) was detected.

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy.

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

The effects of bolt length on penetration hole characteristics, brain damage and specified-risk material dispersal in finished cattle stunned with a penetrating captive bolt stunner.

The purpose of this study was to determine the effect of captive bolt lengths on penetration hole characteristics, brain damage, and specified risk material (SRM) dispersal. Cattle were stunned with a pneumatic captive bolt stunner using: standard (15.2 cm; STRD), medium (16.5 cm; MED), or long (17.8 cm; LON) bolts. Heads (N = 293) and exsanguination blood (N = 103) were collected for analyses. Penetration hole diameter and depth differed by treatment (P ≤ 0.004); both parameters were greatest for LON (P < 0.05). Presence of damage in frontal, parietal, and occipital lobes, olfactory bulb, and collective area including the corpus callosum, fornix and thalamus were impacted by treatment (P < 0.003). Treatment did not impact SRM dispersal (P = 0.33), determined by presence of glial acidic fibrillary protein. Data suggest that bolt length affects both the extent of brain damage and the specific structures damaged but all bolt lengths are successful in causing substantial brain damage and subsequent insensibility.

GPU-powered model analysis with PySB/cupSODA.

SUMMARY: A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator. AVAILABILITY AND IMPLEMENTATION: The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org. CONTACT: paolo.cazzaniga@unibg.it or c.lopez@vanderbilt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer.

BACKGROUND: Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. METHODS: We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. RESULTS: The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10-7; hazard ratio (HR) 37.9, 95% confidence interval 4.1-353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). CONCLUSIONS: This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.