Omgaan met somberheid op oudere leeftijd: een kwalitatieve studie

Background

To gain new insights for support for older people with low mood, we explored the perceptions of ‘screenpositive’ older people on underlying causes and possible solutions.

Design and method

We conducted two in-depth interviews with 38 participants (≥77 years) who screened positive for depressive symptoms in general practice. To investigate the influence of the presence of complex health problems, we included 19 persons with and 19 without complex problems. Complex problems were defined as a combination of functional, somatic, psychological or social problems.

Results

All participants used several cognitive, social or practical coping strategies. Four patterns emerged: mastery, acceptance, ambivalence, and need for support. Some participants, especially those with complex problems, were ambivalent about possible interventions.

Conclusion

Most older participants perceived their coping strategies as sufficient. General practitioners can support self-management by exploring the (effectiveness of) personal coping strategies, providing information, elaborating on perceptions of risks and discussing alternative options with older persons.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Grass vs. tree origin of soil organic carbon under different land-use systems in the Brazilian Cerrado

Aims

This study aimed at assessing whether patch type (i.e., under-shrub soil patch and inter-shrub soil patch) has an effect on soil microbes and how different shrub species altered the soil microbes through understanding soil microbial activity, biomass, and community structure.

Methods

We characterized the soil microbes in under-shrub and inter-shrub soil patches in three shrublands (Artemisia ordosica, Salix psammophila, and Caragana microphylla), respectively, in the Mu Us Desert, China, using microbial activity indicators, chloroform fumigation-extraction analysis, and high-throughput 16S rRNA gene sequencing.

Results

Members of the phyla Proteobacteria, Actinobacteria, Acidobacteria, Planctomycetes, Bacteroidetes, Chloroflexi, Firmicutes, and Gemmatimonadetes were dominant. Inter-shrub soil patch differed from under-shrub soil patch in soil bacterial composition, microbial enzyme activity, and biomass, but not in diversity. Soil collected in A. ordosica shrubland exhibited the highest microbial enzyme activity, biomass, and diversity. Shrub species had significant effects on community structure, primarily the relative abundance of Proteobacteria, Actinobacteria, and Bacteroidetes.

Conclusions

The results indicated that both shrub species and patch type had effects on soil microbial communities. In shrub-dominated desert ecosystems, spatial heterogeneity of soil nutrients and moisture might not be the main factors underlying variations in bacterial diversity. The different compositions of microbial communities in various shrublands provide a foundation for further research into the mechanisms of soil organic carbon accumulation.

     

Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Impacts of onshore wind energy production on birds and bats: recommendations for future life cycle impact assessment developments

Purpose

Models for quantifying impacts on biodiversity from renewable energy technologies are lacking within life cycle impact assessment (LCIA). We aim to provide an overview of the effects of wind energy on birds and bats, with a focus on quantitative methods. Furthermore, we investigate and provide the necessary background for how these can be integrated into new developments of LCIA models in future.

Methods

We reviewed available literature summarizing the effects of wind energy developments on birds and bats. We provide an overview of available quantitative assessment methods that have been employed outside of the LCIA framework to model the different impacts of wind energy developments on wildlife. Combining the acquired knowledge on impact pathways and associated quantitative methods, we propose possibilities for future approaches for a wind energy impact assessment methodology for LCIA.

Results and discussion

Wind energy production has impacts on terrestrial biodiversity through three main pathways: collision, disturbance, and habitat alterations. Birds and bats are consistently considered the most affected taxonomic groups, with different responses to the before-mentioned impact pathways. Outside of the LCIA framework, current quantitative impact assessment prediction models include collision risk models, species distribution models, individual-based models, and population modeling approaches. Developed indices allow scaling of species-specific vulnerability to mortality, disturbance, and/or habitat alterations.

Conclusions

Although insight into the causes behind collision risk, disturbance, and habitat alterations for bats and birds is still limited, the current knowledge base enables the development of a robust assessment tool. Modeling the impacts of habitat alterations, disturbance, and collisions within an LCIA framework is most appropriate using species distribution models as those enable the estimation of species’ occurrences across a region. Although local-scale developments may be more readily feasible, further up-scaling to global coverage is recommended to allow comparison across regions and technologies, and to assess cumulative impacts.

     

Recurrent Nicolau syndrome associated with subcutaneous glatiramer acetate injection—a case report

Background

Glatiramer acetate is worldwide used as first line treatment in relapsing remitting multiple sclerosis. Local skin reactions associated with glatiramer acetate are common, however, only isolated cases of severe local injection site reactions known as Nicolau Syndrome have been reported so far.

Case presentation

We describe the case of a recurrent Nicolau Syndrome occurred during longstanding glatiramer acetate treatment in a woman with multiple sclerosis. The haemorrhagic patch necrotized and was treated locally as a deep second degree burn with excision of dead skin tissue and was healed. Treatment with glatiramer acetate was definitely suspended.

Conclusions

GA injections can be complicated by isolated or recurrent Nicolau Syndrome, a potentially life-threatening condition of which neurologists should be aware.

     

Siberian plants: untapped repertoire of bioactive endosymbionts

Background

Endosymbionts are microorganisms present in all plant species, and constitute the subject of interest among the scientific community. These symbionts have gained considerable attention in recent years, owing to their emerging biological roles. Global challenges, such as antimicrobial resistance, treatment of infectious diseases such as HIV and tuberculosis, cancer, and many genetic disorders, exist. Endosymbionts can help address these challenges by secreting valueadded bioactive compounds with various activities.

Objective

Herein, we describe the importance of plants inhabiting Siberian niches. These plants are considered to be among the least studied organisms in the plant kingdom worldwide. Barcoding these plants can be of interest for exploring bioactive endosymbionts possessing myriad biological properties.

Methods

A systematic survey of relevant scientific reports was conducted using the PubMed search engine. The reports were analyzed, and compiled to draft this review.

Results

The literature survey on Siberian plants regarding endosymbionts included a few reports, since extremely few exploratory studies have been conducted on the plants in these regions. Studies on the endosymbionts of these plants are highly valuable, as they report potent endosymbionts possessing numerous biological properties. Based on these considerations, this review aims to create awareness among the global scientific community working on related areas.

Conclusion

This review could provide the basis for barcoding novel endosymbionts of Siberian plants and their ecological importance, which can be exploited in various sectors. The main purpose of this review is to create awareness of Siberian plants, which are among the least studied organisms in the plant kingdom, with respect to endosymbionts, among the scientific community.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

Metabolomic analysis of pollen from honey bee hives and from canola flowers

Background and aims

Pollen is essential for successful plant reproduction and critical for plant-pollinator mutualisms, as pollen is essential larval nutrition. However, we understand very little about the chemical constituents of pollen leading us to this exploratory study characterizing plant and beehive pollen.

Methods

We performed a metabolomics assay of canola flower pollen and beehive pollen.

Results and discussion

The metabolome of canola pollen is affected by irrigation showing differences in lipids and non-polar secondary metabolites. Metabolome of beehive pollen is affected by plant source showing differences in pentose sugars, myo-inositol and furanose. Further research is needed to document the nutritional bases of plant-pollinator mutualism.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics

Introduction

Ultrasound examination coupled with fine-needle aspiration (FNA) cytology is the gold standard for the diagnosis of thyroid cancer. However, about 10–40% of these analyses cannot be conclusive on the malignancy of the lesions and lead to surgery. The cytological indeterminate FNA biopsies are mainly constituted of follicular—patterned lesions, which are benign in 80% of the cases.

Objectives

The development of a FNAB classification approach based on the metabolic phenotype of the lesions, complementary to cytology and other molecular tests in order to limit the number of patients undergoing unnecessary thyroidectomy.

Methods

We explored the potential of a NMR-based metabolomics approach to improve the quality of the diagnosis from FNABs, using thyroid tissues collected post-surgically.

Results

The NMR-detected metabolites were used to produce a robust OPLSDA model to discriminate between benign and malignant tumours. Malignancy was correlated with amino acids such as tyrosine, serine, alanine, leucine and phenylalanine and anti-correlated with myo-inositol, scyllo-inositol and citrate. Diagnosis accuracy was of 84.8% when only indeterminate lesions were considered.

Conclusion

These results on model FNAB indicate that there is a clear interest in exploring the possibility to export NMR metabolomics to pre-surgical diagnostics.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Using an NMR metabolomics approach to investigate the pathogenicity of amyloid-beta and alpha-synuclein

Introduction

The pathogenicity at differing points along the aggregation pathway of many fibril-forming proteins associated with neurodegenerative diseases is unclear. Understanding the effect of different aggregation states of these proteins on cellular processes is essential to enhance understanding of diseases and provide future options for diagnosis and therapeutic intervention.

Objectives

To establish a robust method to probe the metabolic changes of neuronal cells and use it to monitor cellular response to challenge with three amyloidogenic proteins associated with neurodegenerative diseases in different aggregation states.

Method

Neuroblastoma SH-SY5Y cells were employed to design a robust routine system to perform a statistically rigorous NMR metabolomics study into cellular effects of sub-toxic levels of alpha-synuclein, amyloid-beta 40 and amyloid-beta 42 in monomeric, oligomeric and fibrillar conformations.

Results

This investigation developed a rigorous model to monitor intracellular metabolic profiles of neuronal cells through combination of existing methods. This model revealed eight key metabolites that are altered when neuroblastoma cells are challenged with proteins in different aggregation states. Metabolic pathways associated with lipid metabolism, neurotransmission and adaptation to oxidative stress and inflammation are the predominant contributors to the cellular variance and intracellular metabolite levels. The observed metabolite changes for monomer and oligomer challenge may represent cellular effort to counteract the pathogenicity of the challenge, whereas fibrillar challenge is indicative of system shutdown. This implies that although markers of stress are more prevalent under oligomeric challenge the fibrillar response suggests a more toxic environment.

Conclusion

This approach is applicable to any cell type that can be cultured in a laboratory (primary or cell line) as a method of investigating how protein challenge affects signalling pathways, providing additional understanding as to the role of protein aggregation in neurodegenerative disease initiation and progression.