Visual hallucinations after resection of cerebral metastases: two patients with complex phantom images

PURPOSE

Complex visual hallucinations are rarely seen in neurooncology. They are commonly observed alongside psychotic symptoms in schizophrenia or dementia, in Parkinson's or Lewy-body disease, after opioid medications or anesthesia, and, in particular, they appear with visual impairments.

METHODS

Here we report two normal-sighted and mentally healthy patients with unusual visual hallucinations after the resection and irradiation of brain metastases, the main features of which were persistent colorful and meaningful images with hallucinatory perseveration.

RESULTS

These cases demonstrate the occurrence of complex visual hallucinations after resection of visual cortices as an effect of deafferentation, so-called visual release hallucinations or phantom images, similar to phantom pain after amputation of a limb.

CONCLUSION

This case serves to heighten awareness in the radiooncology practitioner of the occurrence of visual release hallucinations (Charles Bonnet syndrome) related to multidisciplinary treatment of brain metastases.

The Lucy Thermal Emission Spectrometer (L'TES) Instrument

The Lucy Thermal Emission Spectrometer (L'TES) will provide remote measurements of the thermophysical properties of the Trojan asteroids studied by the Lucy mission. L'TES is build-to-print hardware copy of the OTES instrument flown on OSIRIS-REx. It is a Fourier Transform spectrometer covering the spectral range 5.71-100 μm (1750-100 cm-1) with spectral sampling intervals of 8.64, 17.3, and 34.6 cm-1 and a 7.3-mrad field of view. The L'TES telescope is a 15.2-cm diameter Cassegrain telescope that feeds a flat-plate Michelson moving mirror mounted on a linear voice-coil motor assembly to a single uncooled deuterated l-alanine doped triglycine sulfate (DLATGS) pyroelectric detector. A significant firmware change from OTES is the ability to acquire interferograms of different length and spectral resolution with acquisition times of 0.5, 1, and 2 seconds. A single ∼0.851 μm laser diode is used in a metrology interferometer to provide precise moving mirror control and IR sampling at 772 Hz. The beamsplitter is a 38-mm diameter, 1-mm thick chemical vapor deposited diamond with an antireflection microstructure to minimize surface reflection. An internal calibration cone blackbody target, together with observations of space, provides radiometric calibration. The radiometric precision in a single spectrum is ≤2.2 × 10-8 W cm-2 sr-1 /cm-1 between 300 and 1350 cm-1. The absolute temperature error is <2 K for scene temperatures >75 K. The overall L'TES envelope size is 37.6 × 29.0 × 30.4 cm, and the mass is 6.47 kg. The power consumption is 12.6 W average. L'TES was developed by Arizona State University with AZ Space Technologies developing the electronics. L'TES was integrated, tested, and radiometrically calibrated on the Arizona State University campus in Tempe, AZ. Initial data from space have verified the instrument's radiometric and spatial performance.

Machine learning models in trusted research environments - understanding operational risks

Introduction

Trusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.

Objectives

As part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.

Methods

We demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.

Results

We show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.

Conclusions

At this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.

Health-related quality of life, functional outcomes, and complications after sentinel lymph node biopsy and elective neck dissection in early oral cancer: A systematic review

BACKGROUND

Elective neck dissection improves survival in early oral cancer. Sentinel lymph node biopsy may also do this with less morbidity. This systematic review compared health-related quality of life, functional outcomes, and complications after sentinel lymph node biopsy and elective neck dissection in early oral cancer.

METHODS

PRISMA guidelines were followed. Thirteen studies met inclusion criteria.

RESULTS

Results favoring sentinel lymph node biopsy were found in complications, scar length and appearance, length of hospital stay, time to drain removal, and objective shoulder measures at timepoints up to 12 months. Where differences in health-related quality of life were found, methodological issues make their clinical significance questionable.

CONCLUSIONS

Sentinel lymph node biopsy was associated with fewer complications and statistically better outcomes in a number of physical measures. There is as yet no strong evidence to suggest it is associated with better health-related quality of life outcomes. While a number of health-related quality of life outcome measures show promise, their interpretation is hampered by methodological concerns. Further rigorous research is required to address this.

Treatment priorities and regret in older adults with head and neck cancer: A systematic review

BACKGROUND

The majority of head and neck cancer (HNC) diagnoses are seen in people aged 70 and older; these numbers are set to increase. Greater understanding of treatment needs of older patients with HNC is essential. These older patients often have co-existing health conditions, are prone to frailty and may not prioritise survival when considering treatment options. This systematic review examines the current research with regard to priorities and factors influencing treatment regret in older people with HNC.

METHODS

Studies were eligible for inclusion if they (i) reported data from patients with a mean age of 65 years or older who had a confirmed diagnosis of HNC and had been treated using surgery, chemotherapy and/or radiotherapy with either palliative or curative intent, (ii) considered patient's priorities or preferences or examined treatment regret as one of the primary outcomes of the study (iii) were published in English.

RESULTS

Pilot search identified n = 7222 articles; however, following screening, only four papers met the inclusion criteria. Narrative synthesis was indicated to analyse quantitative and qualitative evidence in parallel, as meta-analyses were not possible.

DISCUSSION

There is a paucity in the literature examining older adults with HNC. There is an indication that older adults prioritise maintaining independence when making treatment decisions and treatment regret is seen in those with high levels of depression with level of frailty also a contributing factor. Clinicians should consider patient's social circumstances, premorbid status and priorities in maintaining independence and managing symptoms when making treatment decisions in this cohort.

What MRI Sequences are Necessary for Automated Neural Network-Based Metastasis Segmentation - An Ablation Study

PURPOSE/OBJECTIVE(S)

Brain metastasis (BM) delineation is a time-consuming process in both daily clinical practice and research. Automated BM segmentation algorithms can be used to assist in this task. Most approaches to brain tumor segmentation, such as algorithms trained on the BraTS challenge, use four magnetic resonance imaging (MRI) sequences as input, making them susceptible to missing or corrupted sequences and increase the number of sequences necessary for MRI RT planning. The goal of this project is to compare neural networks with different combinations of input sequences for the segmentation of the contrast-enhancing metastasis and the surrounding FLAIR hyperintense edema. All models were tested in a multicenter international external test cohort. This allows us to determine which MRI sequences are needed for effective automated segmentations.

MATERIALS/METHODS

In total, we had T1-weighted sequences without (T1) and with contrast enhancement (T1-CE), T2-weighted sequences (T2), and T2 fluid-attenuated inversion recovery (FLAIR) sequences from 339 patients with at least one brain metastasis from seven centers available. Preprocessing yielded co-registered, skull-stripped sequences with an isotropic resolution of 1 millimeter. The contrast-enhancing metastasis as well as the surrounding FLAIR hyperintense edema were manually segmented to create reference labels. A baseline 3D U-Net with all four sequences as well as six additional U-Nets with different clinically plausible combinations (T1-CE; T1; FLAIR; T1-CE+FLAIR; T1-CE+T1+FLAIR; T1-CE+T1) of input sequences were trained on a cohort of 239 patients from two centers and subsequently tested on an external cohort of 100 patients from the remaining five centers.

RESULTS

All models that included T1-CE in their selected sequences showed similar performance for metastasis segmentation with a median Dice similarity coefficient (DSC) of 0.93-0.96. T1-CE alone likewise achieved a performance of 0.96 (IQR 0.93-0.97). The model trained with only FLAIR performed worse (DSC = 0.73, IQR 0.54-0.84). For edema segmentation, models that included both T1-CE and FLAIR performed best (median DSC = 0.93), while the remaining four models without simultaneous inclusion of these two sequences (T1-CE; T1; FLAIR; T1-CE+T1) reached a median DSC of 0.81-0.89.

CONCLUSION

Automatic segmentation of brain metastases with less than four input sequences is feasible with minimal or no loss of quality. A T1-CE-only protocol suffices for metastasis segmentation. In contrast, for edema segmentation, the combination of T1-CE and FLAIR seems to be important. Missing either T1-CE or FLAIR decreases performance. These findings may improve future imaging routines by omitting unnecessary sequences, thus speeding up procedures in daily clinical practice while allowing for optimal neural network-based target definitions.

Post-Operative Stereotactic Radiotherapy for Resected Brain Metastases: Results of the Multicenter Analysis (AURORA) of the German Working Group "Stereotactic Radiotherapy"

PURPOSE/OBJECTIVE(S)

While the results of prospective studies support the use of postoperative stereotactic radiotherapy (RT) to the resection cavity (RC) as the standard of care after surgery, there are several issues that need to be investigated such as factors for improving local control, risk of leptomeningeal disease and radiation necrosis. Further, the optimal dose and fractionation is still under debate.

MATERIALS/METHODS

The working group "Stereotactic Radiotherapy" of the German Society of Radiation Oncology (DEGRO) analyzed its multi-institutional database with 661 patients who received postoperative stereotactic RT to the RC. Treatment was performed at 13 centers between 2008 and 2021. Patient characteristics, treatment details, and follow-up data including overall survival (OS), local control (LC) were evaluated. Cox Regression and Kaplan-Meier curves with Log-rank Tests were calculated for selected variables.

RESULTS

In this retrospective study, overall survival was 61.5% at 1 year, 47.6% at 2 years, and 35.5% at 3 years, and local control was 84.6% at 1 year, 74.8% at 2 years, and 72.8% at 3 years. 96% of patients were treated with hypofractionated stereotactic radiotherapy (HSRT), only 26 patients received single fraction radiosurgery (4%). Prognostic factors associated with overall survival were Karnofsky Performance Status, RPA and GPA class, controlled primary tumor and absence of extracranial metastases, whereas prognostic factor associated with local control was planning target volume (23 mL or less).

CONCLUSION

HSRT is the most common fractionation form in the treatment of RCs in this multicenter analysis. This approach results in excellent OS and LC outcomes. OS in patients with resected brain metastases is mainly influenced by performance status. In regard to local control, RT of large cavities remain a challenge with significantly worse outcome.

Induction of cancer neoantigens facilitates development of clinically relevant models for the study of pancreatic cancer immunobiology

Neoantigen burden and CD8 T cell infiltrate are associated with clinical outcome in pancreatic ductal adenocarcinoma (PDAC). A shortcoming of many genetic models of PDAC is the lack of neoantigen burden and limited T cell infiltrate. The goal of the present study was to develop clinically relevant models of PDAC by inducing cancer neoantigens in KP2, a cell line derived from the KPC model of PDAC. KP2 was treated with oxaliplatin and olaparib (OXPARPi), and a resistant cell line was subsequently cloned to generate multiple genetically distinct cell lines (KP2-OXPARPi clones). Clones A and E are sensitive to immune checkpoint inhibition (ICI), exhibit relatively high T cell infiltration, and have significant upregulation of genes involved in antigen presentation, T cell differentiation, and chemokine signaling pathways. Clone B is resistant to ICI and is similar to the parental KP2 cell line in terms of relatively low T cell infiltration and no upregulation of genes involved in the pathways noted above. Tumor/normal exome sequencing and in silico neoantigen prediction confirms successful generation of cancer neoantigens in the KP2-OXPARPi clones and the relative lack of cancer neoantigens in the parental KP2 cell line. Neoantigen vaccine experiments demonstrate that a subset of candidate neoantigens are immunogenic and neoantigen synthetic long peptide vaccines can restrain Clone E tumor growth. Compared to existing models, the KP2-OXPARPi clones better capture the diverse immunobiology of human PDAC and may serve as models for future investigations in cancer immunotherapies and strategies targeting cancer neoantigens in PDAC.

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

TopNEXt: Automatic DDA Exclusion Framework for Multi-Sample Mass Spectrometry Experiments

MOTIVATION

Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) experiments aim to produce high quality fragmentation spectra which can be used to annotate metabolites. However, current Data-Dependent Acquisition (DDA) approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input.

RESULTS

We present TopNEXt, a real-time scan prioritisation framework that improves data acquisition in multi-sample LC-MS/MS metabolomics experiments. TopNEXt extends traditional DDA exclusion methods across multiple samples by using a Region of Interest (RoI) and intensity-based scoring system. Through both simulated and lab experiments we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts.

AVAILABILITY

TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914. Data can be found at 10.5525/gla.researchdata.1382.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities

Introduction

Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure environments in which researchers can access sensitive personal data and develop AI (in particular machine learning (ML)) models. However, currently few TREs support the training of ML models in part due to a gap in the practical decision-making guidance for TREs in handling model disclosure. Specifically, the training of ML models creates a need to disclose new types of outputs from TREs. Although TREs have clear policies for the disclosure of statistical outputs, the extent to which trained models can leak personal training data once released is not well understood.

Background

We review, for a general audience, different types of ML models and their applicability within healthcare. We explain the outputs from training a ML model and how trained ML models can be vulnerable to external attacks to discover personal data encoded within the model.

Risks

We present the challenges for disclosure control of trained ML models in the context of training and exporting models from TREs. We provide insights and analyse methods that could be introduced within TREs to mitigate the risk of privacy breaches when disclosing trained models.

Discussion

Although specific guidelines and policies exist for statistical disclosure controls in TREs, they do not satisfactorily address these new types of output requests; i.e., trained ML models. There is significant potential for new interdisciplinary research opportunities in developing and adapting policies and tools for safely disclosing ML outputs from TREs.

The Evaluation of Training Oral and Maxillofacial Trainees in Head and Neck Cancer Doctor-Patient Communication Using the Patient Concerns Inventory

Head and neck cancer has a significant impact on a patient's health related quality of life (HRQOL). The head and neck specific Patient Concerns Inventory (PCI-HN) has been utilised to enhance doctor-patient dialogue in routine consultations. To date there has been no formal training for oral and maxillofacial surgery (OMFS) surgical trainees in the use of the PCI-HN in consultations. The aim of the study was to evaluate training for OMFS surgical trainees in the use of the PCI-HN, using simulated follow-up HNC consultations, in order to improve doctor-patient communication skills.

MATERIAL AND METHODS

Ten oral and maxillofacial surgical trainees completed actor simulated HNC consultations before and after training. A study-specific mark scheme was developed based on the ComOn-Coaching rating scales and used to score the doctor-patient interaction. A group debrief afterwards explored the trainee's experiences of the training and consultations.

RESULTS

All trainees showed an improvement in doctor-patient communication scores following their training. Overall, the six participants who were Specialty registrars, year 3 (ST3) or above, scored higher, than the four Specialty registrars, year 1-2 (ST1-2). The scores were higher if fewer PCI-HN items were discussed (3-4). The most frequently avoided PCI-HN items were intimacy and relationships. The trainees considered that their training was useful for organising their consultations and for providing holistic care.

CONCLUSION

Although training improved surgeon-patient communication, further evaluation is required with a larger number of trainees and actual consultations in clinic.

Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

Tyrosine Kinase Inhibitor Independent Gene Expression Signature in CML Offers New Targets for LSPC Eradication Therapy

Tyrosine kinase inhibitors (TKI) have revolutionised the treatment of CML. However, TKI do not eliminate the leukaemia stem cells (LSC), which can re-initiate the disease. Thus, finding new therapeutic targets in CML LSC is key to finding a curative treatment. Using microarray datasets, we defined a list of 227 genes that were differentially expressed in CML LSC compared to the healthy controls but were not affected by TKI in vitro. Two of them, CD33 and PPIF, are targeted by gemtuzumab-ozogamicin and cyclosporin A, respectively. We treated CML and the control CD34+ cells with either drug with or without imatinib to investigate the therapeutic potential of the TKI-independent gene expression programme. Cyclosporine A, in combination with imatinib, reduced the number of CML CFC compared with non-CML controls, but only at supra-therapeutic concentrations. Gemtuzumab-ozogamicin showed an EC50 of 146 ng/mL, below the plasma peak concentration of 630 ng/mL observed in the AML patients and below the EC50 of 3247 ng/mL observed in the non-CML cells. Interestingly, gemtuzumab-ozogamicin seems to promote cell cycle progression in CML CD34+ cells and demonstrated activation of the RUNX1 pathway in an RNAseq experiment. This suggests that targeting the TKI-independent genes in CML LSC could be exploited for the development of new therapies in CML.

GRAIMatter: Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMatter)

ObjectivesTo assess a range of tools and methods to support Trusted Research Environments (TREs) to assess output from AI methods for potentially identifiable information, investigate the legal and ethical implications and controls, and produce a set of guidelines and recommendations to support all TREs with export controls of AI algorithms. ApproachTREs provide secure facilities to analyse confidential personal data, with staff checking outputs for disclosure risk before publication. Artificial intelligence (AI) has high potential to improve the linking and analysis of population data, and TREs are well suited to supporting AI modelling. However, TRE governance focuses on classical statistical data analysis. The size and complexity of AI models presents significant challenges for the disclosure-checking process. Models may be susceptible to external hacking: complicated methods to reverse engineer the learning process to find out about the data used for training, with more potential to lead to re-identification than conventional statistical methods. ResultsGRAIMatter is: Quantitatively assessing the risk of disclosure from different AI models exploring different models, hyper-parameter settings and training algorithms over common data types Evaluating a range of tools to determine effectiveness for disclosure control Assessing the legal and ethical implications of TREs supporting AI development and identifying aspects of existing legal and regulatory frameworks requiring reform. Running 4 PPIE workshops to understand their priorities and beliefs around safeguarding and securing data Developing a set of recommendations including suggested open-source toolsets for TREs to use to measure and reduce disclosure risk descriptions of the technical and legal controls and policies TREs should implement across the 5 Safes to support AI algorithm disclosure control training implications for both TRE staff and how they validate researchers ConclusionGRAIMatter is developing a set of usable recommendations for TREs to use to guard against the additional risks when disclosing trained AI models from TREs.

Stereotactic radiosurgery and radiotherapy for resected brain metastases: current pattern of care in the Radiosurgery and Stereotactic Radiotherapy Working Group of the German Association for Radiation Oncology (DEGRO)

PURPOSE

Preoperative stereotactic radiosurgery (SRS) of brain metastases may achieve similar local control and better leptomeningeal control rates than postoperative fractionated stereotactic radiotherapy (FSRT) in patients treated with elective metastasectomy. To plan a multicentre trial of preoperative SRS compared with postoperative FSRT, a survey of experts was conducted to determine current practice.

METHODS

A survey with 15 questions was distributed to the DEGRO Radiosurgery and Stereotactic Radiotherapy Working Group. Participants were asked under what circumstances they offered SRS, FSRT, partial and/or whole brain radiotherapy before or after resection of a brain metastasis, as well as the feasibility of preoperative stereotactic radiosurgery and neurosurgical resection within 6 days.

RESULTS

Of 25 participants from 24 centres, 22 completed 100% of the questions. 24 respondents were radiation oncologists and 1 was a neurosurgeon. All 24 centres have one or more dedicated radiosurgery platform and all offer postoperative FSRT. Preoperative SRS is offered by 4/24 (16.7%) centres, and 9/24 (37.5%) sometimes recommend single-fraction postoperative SRS. Partial brain irradiation is offered by 8/24 (33.3%) centres and 12/24 (50%) occasionally recommend whole-brain irradiation. Two centres are participating in clinical trials of preoperative SRS. SRS techniques and fractionation varied between centres.

CONCLUSION

All responding centres currently offer postoperative FSRT after brain metastasectomy. Approximately one third offer single-fraction postoperative SRS and four already perform preoperative SRS. With regard to potential co-investigators, 18 were identified for the PREOP‑2 multicentre trial, which will randomise between preoperative SRS and postoperative FSRT.

Scottish Medical Imaging Service - Technical and Governance controls

ObjectivesThe Scottish Medical Imaging (SMI) service provides linkable, population based, “research-ready” real-world medical images for researchers to develop or validate AI algorithms within the Scottish National Safe Haven. The PICTURES research programme is developing novel methods to enhance the SMI service offering through research in cybersecurity and software/data/infrastructure engineering. ApproachAdditional technical and governance controls were required to enable safe access to medical images. The researcher is isolated from the rest of the trusted research environment (TRE) using a Project Private Zone (PPZ). This enables researchers to build and install their own software stack, and protects the TRE from malicious code. Guidelines are under development for researchers on the safe development of algorithms and the expected relationship between the size of the model and the training dataset. There is associated work on the statistical disclosure control of models to enable safe release of trained models from the TRE. ResultsA policy enabling the use of “Non-standard software” based on prior research, domain knowledge and experience gained from two contrasting research studies was developed.  Additional clauses have been added to the legal control – the eDRIS User Agreement – signed by each researcher and their Head of Department.  Penalties for attempting to import or use malware, remove data within models or any attempt to deceive or circumvent such controls are severe, and apply to both the individual and their institution. The process of building and deploying a PPZ has been developed allowing researchers to install their own software. No attempt has yet been made to add additional ethical controls; however, a future service development could be validating the performance of researchers’ algorithms on our training dataset. ConclusionThe availability to conduct research using images poses new challenges and risks for those commissioning and operating TREs. The Private Project Zone and our associated governance controls are a huge step towards supporting the needs of researchers in the 21st century.

The experiences of new mothers accessing feeding support for infants with down syndrome during the COVID-19 pandemic

Infants with Down syndrome are more likely to experience feeding problems and mothers are likely to require more feeding support than mothers of typically developing infants. During the COVID-19 pandemic, many feeding support services changed from face-to-face to online, which impacted some maternal feeding experiences negatively, but no studies to date have explored the impact for mothers of infants with Down syndrome. Thematic analysis was conducted on semi-structured interviews from thirteen new mothers of infants (aged 8-17 months) with Down syndrome in the UK. Three superordinate themes were generated: (1) Every baby with Down syndrome has a unique journey, (2) There's no point asking, they won't know, (3) Lack of in-person support. Many mothers expressed frustrations over health professionals' lack of Down syndrome specific knowledge resulting in unmet needs, further magnified due to the nature of the virtual support being offered. Moreover, mothers struggled with reduced social support from other mothers when encountering feeding problems. These results hold real-world implications for health professionals who could provide more specialised face-to-face feeding support to mothers of infants with Down syndrome. This should be prioritised for children's overall development and mothers' wellbeing.

Using topic modeling to detect cellular crosstalk in scRNA-seq

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complemantary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

1082 Are Trauma and Orthopaedics Consultant Posts Decreasing in Frequency and Increasing in Subspecialisation?

Aim

Changes to the configuration of the NHS Trauma Services in T&O and the level of subspecialisation are changing and could impact training and the number of consultant posts available at CCT. The aim of this study was to review the number of T&O Consultant posts and determine the frequency of the subspecialist requirements of these posts from 2011 to 2020, to assess the perceived reduction in advertised numbers and the increasing sub specialisation as a whole.

Method

451 volumes of the BMJ were reviewed over Jan 2011 - April 2020. The total number of jobs advertised were recorded. Additionally, for each post, sub-specialism, regional analysis and substansive or locum tenure were recorded.

Results

1141 jobs were advertised during this period. There has been a decreasing trend in the total number of advertised posts of 13% per annum. Proportion of specialty jobs remain constant yearly. However, jobs listing Trauma clearly decreased after 2012, when trauma centres were initiated. Clear decreases were present in North-west and Scotland in number of jobs. There has also been an increasing trend of advertisement of substantive posts, with locum posts forming less than 13% of total advertisements.

Conclusions

We found no significant increase in job specialism contradicting previous research. Overall, the total number of Consultant jobs in Trauma and Orthopaedics is decreasing, especially in the north-west and Scotland, two of the biggest employers. Questions must be asked for the future of training in T&O to meet service needs.

Assessing potential routes of Streptococcus agalactiae transmission between dairy herds using national surveillance, animal movement data and molecular typing

Streptococcus agalactiae, also known as group B Streptococcus (GBS), is a pathogen of humans and animals. It is an important cause of mastitis in dairy cattle, causing decreased milk quality and quantity. Denmark is the only country to have implemented a national surveillance and control campaign for GBS in dairy cattle. After a significant decline in the 20th century, prevalence has increased in the 21st century. Using a unique combination of national surveillance, cattle movement data and molecular typing, we tested the hypothesis that transmission mechanisms differ between GBS strains that are almost exclusive to cattle and those that affect humans as well as cattle, which would have implications for control recommendations. Three types of S. agalactiae, sequence type (ST) 1, ST23 and ST103 were consistently the most frequent strains among isolates obtained through the national surveillance programme from 2009 to 2011. Herds infected with ST103, which is common in cattle but rarely found in people in Europe, were spatially clustered throughout the study period and across spatial scales. By contrast, strains that are also commonly found in humans, ST1 and ST23, showed no spatial clustering in most or any years of the study, respectively. Introduction of cattle from a positive herd was associated with increased risk of infection by S. agalactiae in the next year (risk ratio of 2.9 and 4.7 for 2009-2010 and 2010-2011, respectively). Moreover, mean exposure to infection was significantly higher for newly infected herds and significantly lower for persistently susceptible herds, as compared to random simulated networks with the same properties, which suggests strong association between the cattle movement network and new infections. At strain-level, new infections with ST1 between 2009 and 2010 were significantly associated with cattle movements, while other strains showed only some degree of association. Sharing of veterinary services, which may serve as proxy for local or regional contacts at a range of scales, was not significantly associated with increased risk of introduction of S. agalactiae or one of the three predominant strains on a farm. Our findings support the reinstatement of restrictions on cattle movements from S. agalactiae positive herds, which came into effect in 2018, but provide insufficient evidence to support strain-specific control recommendations.

Five fraction stereotactic radiotherapy after brain metastasectomy: a single-institution experience and literature review

PURPOSE

The outcomes of five fraction stereotactic radiotherapy (hfSRT) following brain metastasectomy were evaluated and compared with published series.

METHODS

30 Gy in 5 fractions HfSRT prescribed to the surgical cavity was reduced to 25 Gy if the volume of 'brain-GTV' receiving 20 Gy exceeded 20 cm³. Endpoints were local recurrence, nodular leptomeningeal recurrence, new brain metastases and radionecrosis. The literature was searched for reports of clinical and dosimetric outcomes following postoperative hfSRT in 3-5 fractions.

RESULTS

39 patients with 40 surgical cavities were analyzed. Cavity local control rate at 1 year was 33/40 (82.5%). 3 local failures followed 30 Gy/5 fractions and 4 with 25 Gy/5 fractions. The incidence of leptomeningeal disease (LMD) was 7/40 (17.5%). No grade 3-4 toxicities, particularly no radionecrosis, were reported. The incidence of distant brain metastases was 15/40 (37.5%). The median overall survival was 15 months. Across 13 published series, the weighted mean local control was 83.1% (adjusted for sample size), the mean incidence of LMD was 14.9% (7-34%) and the mean rate of radionecrosis was 10.3% (0-20.6%).

CONCLUSION

Postoperative hfSRT can be delivered with 25-30 Gy in 5 fractions with efficacy in excess of 82% and no significant toxicity when the dose to 'brain-GTV' does not exceed 20 cm³.

Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification

Motivation

Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS²).

Results

We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS² data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS² measurements available besides MS¹.

Availability and implementation

Software and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration.

Supplementary information

Supplementary data are available at Bioinformatics online.

P–123 How to develop accurate Computer Assisted Sperm Analysis (CASA) AI in the absence of protocol standardization and abundance of human error when performing semen analyses?

Study question

How can an automation & artificial intelligent tools be developed to perform according to WHO recommendations?

Summary answer

Developing CASA performs at &lt; 20% error margin requires AI trained with high quality datasets and a robotic system adheres to WHO guidelines.

What is known already

A survey of 40 andrology laboratories, in 22 countries, revealed that &gt; 90% had nonconformities in correct use of equipment, standardisation of protocols and quality control, leading to a lack of compliance to WHO protocols. Conventional CASA systems can standardize analysis, but controversy has occurred due to differences between manual and automated analyses stemming from: 1) all cells in a semen sample are detected including debris; 2) protocol variation when compared to top-notch manual analysis. The first point can be addressed by AI. The second point can be addressed by robotics designed to adhere to WHO guidelines.

Study design, size, duration

A mojo AISA (AI-powered semen analysis) system was placed in four clinical laboratories mentioned above capturing images of over 300 samples, one million images were generated over a course of 2 years. Mojo AISA’s AI was trained on data collected from the four clinics using robotic system is developed according to WHO guidelines.

Participants/materials, setting, methods

For an AI to detect sperm accurately, sperm samples were captured using mojo AISA smart microscopy and then the extracted sperm images expertly annotated. To evaluate the system-ability for semen analysis, fresh sample were analysed for concentration and motility by a manual operator and compared to a mojo AISA test.

Main results and the role of chance

To train the sperm detection AI, representative sperm images were carefully captured using mojo AISA and processed according to the following criteria: the number of images and videos to train and to test the model: 50,000 spermatozoon head and tails with various variations the variety of images: data used to train the AI has to be representative of the population that will undergo the analysis: 1) wide concentration ranges from 0 to 300 M/ml, 2) high and low density of debris and cells, 3) Presence of slight aggregations careful and precise annotation: expert andrology scientists annotated sperm images and identify objects to exclude, such as debris in seminal plasma, Mojo AISA is an attempt strictly build CASA AI system to WHO-guidelines. The marriage of AI and robotics automation has shown a promising results to mimic humans when measuring a semen sample and attempt to obtain results comparable to the manual analysis.

mojo AISA’s performance improved three-fold (from 0,85 to 0,95 Pearson sperm count correlation and from &gt;100% means relative error to 25% mean relative error).

Limitations, reasons for caution

Lack of standardization for semen analysis laboratory process globally is a bottleneck towards building a robust multi-center study, on-site CASA testing and generating an actionable data pool for studying the causes behind male fertility declineWider implications of the findings: Key learnings for parties advancing developing AI based on images and videos for application in the fertility space.

Trial registration number

Not applicable

The watery eye quality of life (WEQOL) questionnaire: a patient-reported outcome measure for surgically amenable epiphora

OBJECTIVE OR PURPOSE

To develop and test a patient-reported outcome measure for assessing health-related quality of life (HRQOL) in surgically amenable epiphora.

DESIGN

Questionnaire development and validation study.

PARTICIPANTS

201 patients with a cause of epiphora amenable to surgical intervention, recruited across three independent centres.

METHODS, INTERVENTION OR TESTING

The watery eye quality of life (WEQOL) questionnaire was developed and refined according to defined psychometric standards. Both surgical and non-surgical participants completed WEQOL at baseline and follow-up (>3 months), along with the Lacrimal Symptom Questionnaire (Lac-Q), RAND Short Form Health Survey (SF-36) and Glasgow Benefit Inventory (GBI). Convergent validity of WEQOL was evaluated according to correlation (R > 0.40) with each of these additional tests. Responsiveness of WEQOL to intervention was evaluated according to patient-reported success. Test-retest reliability was assessed by the Bland-Altman method and intraclass correlation (ICC) in a subset of 64 participants at baseline.

MAIN OUTCOME MEASURES

WEQOL construct validity, responsiveness and test-retest reliability.

RESULTS

WEQOL was moderately correlated (R > 0.4) with the Lac-Q and several subscales of the SF-36 (physical role limitation, social, emotional role limitation and emotional well-being). A stronger correlation was found between the change in WEQOL at follow-up and GBI (R = 0.61). An appropriate graded response was found with a significant change in WEQOL score being observed in patients reporting successful (-28%, p < 0.0001) and partially successful surgery (-6%, p = 0.04), but not in those reporting unsuccessful surgery (+2%, p = 0.9). High test-retest reliability was observed (ICC = 0.93).

CONCLUSIONS

The WEQOL questionnaire has been developed systematically according to modern psychometric standards and has been designed to evaluate the quality of life in patients with epiphora that is of a surgically amenable cause. In this study, it has demonstrated appropriate test-retest reliability, responsiveness and construct validity.

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Rapid Development of Improved Data-Dependent Acquisition Strategies

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimize approaches in busy MS facilities. In this work, we first provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost-efficient development of new DDA strategies using a previously developed virtual metabolomics mass spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an Instrument Application Programming Interface (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.

Up-down biphasic volume response of human red blood cells to PIEZO1 activation during capillary transits

In this paper we apply a novel JAVA version of a model on the homeostasis of human red blood cells (RBCs) to investigate the changes RBCs experience during single capillary transits. In the companion paper we apply a model extension to investigate the changes in RBC homeostasis over the approximately 200000 capillary transits during the ~120 days lifespan of the cells. These are topics inaccessible to direct experimentation but rendered mature for a computational modelling approach by the large body of recent and early experimental results which robustly constrain the range of parameter values and model outcomes, offering a unique opportunity for an in depth study of the mechanisms involved. Capillary transit times vary between 0.5 and 1.5s during which the red blood cells squeeze and deform in the capillary stream transiently opening stress-gated PIEZO1 channels allowing ion gradient dissipation and creating minuscule quantal changes in RBC ion contents and volume. Widely accepted views, based on the effects of experimental shear stress on human RBCs, suggested that quantal changes generated during capillary transits add up over time to develop the documented changes in RBC density and composition during their long circulatory lifespan, the quantal hypothesis. Applying the new red cell model (RCM) we investigated here the changes in homeostatic variables that may be expected during single capillary transits resulting from transient PIEZO1 channel activation. The predicted quantal volume changes were infinitesimal in magnitude, biphasic in nature, and essentially irreversible within inter-transit periods. A sub-second transient PIEZO1 activation triggered a sharp swelling peak followed by a much slower recovery period towards lower-than-baseline volumes. The peak response was caused by net CaCl2 and fluid gain via PIEZO1 channels driven by the steep electrochemical inward Ca2+ gradient. The ensuing dehydration followed a complex time-course with sequential, but partially overlapping contributions by KCl loss via Ca2+-activated Gardos channels, restorative Ca2+ extrusion by the plasma membrane calcium pump, and chloride efflux by the Jacobs-Steward mechanism. The change in relative cell volume predicted for single capillary transits was around 10-5, an infinitesimal volume change incompatible with a functional role in capillary flow. The biphasic response predicted by the RCM appears to conform to the quantal hypothesis, but whether its cumulative effects could account for the documented changes in density during RBC senescence required an investigation of the effects of myriad transits over the full four months circulatory lifespan of the cells, the subject of the next paper.

Comparative Metabologenomics Analysis of Polar Actinomycetes

Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the omics era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.

Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

Referral, Assessment and Use of Screening Measures Related to Autism Spectrum Disorder at a Tertiary Hospital Setting

Children with developmental concerns in Australia continue to experience inequitable healthcare and service-related delays, even when diagnostic risk is identified. This study sought to explore service and demographic pathway factors leading up to autism spectrum disorder (ASD) assessment, including value of screening measures applied at triage. Following a trial of centralised intake for referred young children with suspected ASD, observational, retrospective pathway data was explored using bivariate and regression analyses. The mean age of 159 children referred with autism symptoms was 3.6 years, and 64% were diagnosed with ASD. Service allocation was associated with diagnosis, whilst screening tool results were not. Improved pathways are needed to limit wasted waiting times and direct each child to needs-based services.

Imaging digital arteries in systemic sclerosis by tomographic 3-dimensional ultrasound

Objective methods are needed to quantify digital artery disease in systemic sclerosis (SSc) for clinical trials of vascular therapies. Our primary aim was to examine feasibility of a novel tomographic three-dimensional-(3-D) ultrasound (tUS) with high-frequency ultrasound (HFUS) or ultra-high-frequency ultrasound (UHFUS) to assess the digital arteries in patients with SSc compared to healthy controls. A secondary objective was to compare the total wall volume (TWV) as a measure of intimal/medial thickness. Eighteen patients with a confirmed diagnosis of SSc were studied by tUS HFUS (17.5 MHz, n = 10) or tUS UHFUS (48 and 70 MHz, n = 8) with equal numbers of healthy controls of similar age and gender. The majority of patients had limited cutaneous SSc and were representative of a spectrum of digital vasculopathy, with over half (n = 6 HFUS and n = 5 UHFUS) having previous digital ulceration. Over half were receiving oral vasodilatory therapy. TWV was measured in both digital arteries of the middle finger bilaterally. At least, two digital arteries could be identified at 17.5 MHz in all patients and healthy controls. Whereas, at least two digital arteries could be identified in relatively fewer patients compared to healthy controls using 48 MHz (n = 6 and 10) and especially 70 MHz (n = 4 and 10) UHFUS. The median difference in TWV between patients and healthy controls was -6.49 mm³ using 17.5 MHz, 1.9 mm³ at 48 MHz, and -0.4 mm³ at 70 MHz. tUS using UHFUS is a feasible method to measure TWV of digital arteries in SSc. Transducer frequency plays an important factor in successful digital artery measurement, with 48 MHz being the optimal frequency.

R package for statistical inference in dynamical systems using kernel based gradient matching: KGode

Many processes in science and engineering can be described by dynamical systems based on nonlinear ordinary differential equations (ODEs). Often ODE parameters are unknown and not directly measurable. Since nonlinear ODEs typically have no closed form solution, standard iterative inference procedures require a computationally expensive numerical integration of the ODEs every time the parameters are adapted, which in practice restricts statistical inference to rather small systems. To overcome this computational bottleneck, approximate methods based on gradient matching have recently gained much attention. The idea is to circumvent the numerical integration step by using a surrogate cost function that quantifies the discrepancy between the derivatives obtained from a smooth interpolant to the data and the derivatives predicted by the ODEs. The present article describes the software implementation of a recent method that is based on the framework of reproducing kernel Hilbert spaces. We provide an overview of the methods available, illustrate them on a series of widely used benchmark problems, and discuss the accuracy–efficiency trade-off of various regularization methods.

The Missing Link: Developing a pipeline for accelerated antibiotic discovery from Streptomyces through linking ‘omics data

The genus Streptomyces has proven to be a rich reservoir of specialized metabolites, accounting for 80% of all microbially produced antibiotics including chloramphenicol and nystatin from S. venezuelae and S. noursei respectively. However, the discovery of novel microbial chemistry is still greatly needed to combat antimicrobial resistance. Comparative metabolomics, using platforms such as Global Natural Products Social Molecular Networking (GNPS), as well as tools such as antiSMASH and BiGSCAPE have aided the mining of biosynthetic gene clusters (BGC’s) across datasets but comparing the chemistry to the encoding biosynthetic gene clusters is a significant bottleneck.

In this study, ten Streptomyces strains were selected, based on phylogeny and availability of genome sequence. The strains were cultured on 6 types of Actinomycete-specific media to maximise metabolite diversity. Liquid Chromatography tandem Mass Spectrometry (LC-MS/MS) was used to obtain spectral data from crude metabolite extracts enabling comparative metabolomics analysis via the GNPS platform. As the genome sequences were publicly available, genome mining of BGC’s was achieved using antiSMASH resulting in 260 BGC’s across the ten strains. This revealed 53 gene cluster families when analysed using BiGSCAPE, the largest encoding for 8 metabolites.

In future, both biosynthetic (BGC’s) and chemistry (parent ions) datasets will be computationally linked based on strain presence/absence. The development of standardised datasets that enable cross-‘omics comparison will aid prioritisation of novel antibiotics, especially when combined with bioactivity data.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery

Secondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

P1450Deep vein thrombosis after right sided catheter ablation; more common then previously thought?

Funding Acknowledgements

Bristol-Myers Squibb

Background

Right sided cardiac catheter ablation has become an indispensable tool to treat supraventricular cardiac dysrhythmias, with ablation of certain arrhythmias having cure rates over 90%. Due to this the frequency of these procedures is increasing annually and it is imperative we understand the incidence of all complication. One lesser studied complication is that of deep vein thrombosis (DVT), for which catheter ablation demonstrates all elements of Virchow"s triad.  As right sided ablations are carried out to treat troublesome palpitations, not to reduce mortality, it is important all risks are identified, especially those which are themselves potentially life threatening and can be modified.

Purpose

 

To determine the incidence of DVT after right sided cardiac catheter ablation.

Methods

 

We undertook a prospective multi-center study recruiting adult patients undergoing clinically indicated cardiac ablation for atrioventricular nodal re-entrant tachycardia and atrioventricular re-entrant tachycardia with right sided accessory pathway. Important exclusion criteria included patients on anticoagulation or antiplatelet therapy. Participants underwent bilateral compression venous duplex ultrasonography from the inferior vena cava to the popliteal vein to access for DVT at 24 hours and between 10 to 14 days post-procedure. The uncannulated contralateral leg acted as a control.

Result

 

At interim analysis 71 participants had completed the study with average age 47 year (+/- 14), procedure duration 67 minutes, and with a female predominance. Seven patients developed acute DVT in either the femoral or internal iliac vein in the access leg. No thrombus was seen in the control leg. This gives an incidence of 10% (95% CI 4-19%) with p value of 0.023 on Chi-square testing.

Conclusion

We found a statistically significant proportion of patients undergoing right sided cardiac catheter ablation developed acute proximal DVT on ultrasound. All patients were treated with 3 to 6 months of anticoagulation therapy in accordance with NICE guidelines. These results suggest that DVT may occur at a high frequency then previously thought in this cohort and supports the consideration of peri-procedural prophylactic anticoagulation.

Abstract Figure. Acute thrombus in the femoral vein

Predicting host taxonomic information from viral genomes: A comparison of feature representations

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different ‘levels’ of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information.

Elucidating the host of a newly identified virus species is an important challenge, with applications from knowing the source species of a newly emerged pathogen to understanding the bacteriophage-host relationships within the microbiome of any of earth’s ecosystems. Current high throughput methods used to identify viruses within biological or environmental samples have resulted in an unprecedented increase in virus discovery. However, for the majority of these virus genomes the host species/taxonomic classification remains unknown. To address this gap in our knowledge there is a need for fast, accurate computational methods for the assignment of putative host taxonomic information. Machine learning is an ideal approach but to maximise predictive accuracy the viral genomes need to be represented in a format (sets of features) that makes the discriminative information available to the machine learning algorithm. Here, we compare different types of features derived from the same viral genomes for their ability to predict host information. Our results demonstrate that all these feature sets are predictive of host taxonomy and when combined have the potential to improve accuracy over the use of individual feature sets across many virus host prediction applications.

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented, giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses, termed Mass2Motifs, that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of (i) combinatorial in silico matching of experimental mass features to substructures of candidate molecules, and (ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present MotifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database, we can more quickly gain insight into the constituents of complex mixtures. This will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.