Direct deduction of chemical class from NMR spectra.

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. The method identified as suitable for classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found to be suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to spectral interpretation problems.

A pilot study for fragment identification using 2D NMR and deep learning.

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.

Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images.

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients.

Applying NMR compound identification using NMRfilter to match predicted to experimental data.

INTRODUCTION: Metabolomics is the approach of choice to guide the understanding of biological systems and its molecular intricacies, but compound identification is yet a bottleneck to be overcome. OBJECTIVE: To assay the use of NMRfilter for confidence compound identification based on chemical shift predictions for different datasets. RESULTS: We found comparable results using the lead tool COLMAR and NMRfilter. Then, we successfully assayed the use of HMBC to add confidence to the identified compounds. CONCLUSIONS: NMRfilter is currently under development to become a stand-alone interactive software for high-confidence NMR compound identification and this communication gathers part of its application capabilities.

Axial Spondyloarthritis: Overcoming the Barriers to Early Diagnosis-an Early Inflammatory Back Pain Service.

PURPOSE OF REVIEW: To discuss the challenges to early diagnosis of axial spondyloarthritis (axSpA) and present the impact an early inflammatory back pain service (EIBPS) had on diagnostic delay in the UK. RECENT FINDINGS: Diagnostic delay in axSpA varies greatly worldwide, and has continued in the UK at an average of 8.5 years. Education, public awareness, and accessibility to inflammatory back pain (IBP) pathways are some of the key barriers to achieving a prompt diagnosis. A recent national inquiry has highlighted insufficiencies in the availability of specialist axSpA services and limited provision of education and training to first contact practitioners and allied healthcare providers. We demonstrate diagnostic delay in axSpA can be successfully reduced to 3 years when an early inflammatory back pain service is embedded within a rheumatology department alongside a local educational and awareness campaign. Sharing these experiences and outcomes will enable other departments to engage in best practice and achieve similar results, facilitating a timely and accurate diagnosis.

An integrated approach for mixture analysis using MS and NMR techniques.

We suggest an improved software pipeline for mixture analysis. The improvements include combining tandem MS and 2D NMR data for a reliable identification of the constituents in an algorithm based on network analysis aiming for a robust and reliable identification routine. An important part of this pipeline is the use of open-data repositories, although it is not totally reliant on them. The NMR identification step emphasizes robustness and is less sensitive towards changes in data acquisition and processing than existing methods. The process starts with LC-ESI-MSMS based molecular network dereplication using data from the GNPS collaborative collection. We identify closely related structures by propagating structure elucidation through edges in the network. Those identified compounds are added on top of a candidate list for the following NMR filtering method that predicts HSQC and HMBC NMR data. The similarity of the predicted spectra of the set of closely related structures to the measured spectra of the mixture sample is taken as one indication of the most likely candidates for its compounds. The other indication is the match of the spectra to clusters built by a network analysis from the spectra of the mixture. The sensitivity gap between NMR and MS is anticipated and it will be reflected naturally by the eventual identification of fewer compounds, but with a higher confidence level, after the NMR analysis step. The contributions of the paper are an algorithm combining MS and NMR spectroscopy and a robust nJCH network analysis to explore the complementary aspects of both techniques. This delivers good results, even if a perfect computational separation of the compounds in the mixture is not possible. All of the scripts are freely available to aid studies such as with plants, marine organisms, and microorganism natural product chemistry and metabolomics, as those are the driving forces for this project.

Severe gangrene in a patient with anti-RNP positive limited cutaneous systemic sclerosis/rheumatoid arthritis overlap syndrome caused by vasculopathy and vasculitis.

In this paper, we describe a case of a male patient with anti-U1RNP positive limited cutaneous systemic sclerosis/rheumatoid arthritis overlap syndrome, who presented acutely with rapidly progressive digital ischemia, which lead to extensive gangrene. Management with conventional vasodilator therapy was unsuccessful. There were constitutional symptoms and a marked inflammatory response in the absence of evidence of infection, implying a component of vasculitis underlying the presentation. Treatment with immunosuppression and intravenous immunoglobulin led to resolution of the inflammatory process with no further progression of tissue necrosis. Here we discuss pertinent issues raised by the case, including the management of digital ischemia and gangrene in this context and the relevance of the anti-U1RNP in systemic sclerosis overlap syndromes.

Teriparatide and vertebral fracture healing in Ankylosing Spondylitis.

Patients with Ankylosing Spondylitis (AS) are four times more likely to sustain spinal fractures. Due to the associated risk of neurological complications treatment is complex. We present the case of a 56-year-old Caucasian gentleman with AS who sustained a fracture of T2 vertebra following a traumatic hyperextension injury. He declined surgery in fear of complications and started treatment with subcutaneous Teriparatide at a dose of 20 mg daily for six months. There was complete healing of the vertebral fracture at 6 months without any complications. This case is unique as complete healing was achieved without preceding surgical intervention. Further exploration of the use of Teriparatide in spinal fractures in patients with AS is recommended to support the theories generated by this and other existing cases in the literature.

Randomised, prospective, non-blinded pilot study comparing the effect of intramuscular steroid injections and intralesional steroid injections in the management of tennis elbow.

BACKGROUND: Tennis elbow is an overuse injury affecting people performing repetitive forearm movements. It is a soft tissue disorder that causes significant disability and pain. The aim of the study was to establish that an intramuscular steroid injection is effective in the short-term pain relief and functional improvement of tennis elbow. The severity of pain at the injection site was monitored to determine whether the intramuscular injection is better tolerated than the intralesional injection. METHODS AND RESULTS: 19 patients, who had no treatment for tennis elbow in the preceding 3 months, were recruited from Whipps Cross University Hospital, London, and were randomised to receive either 80 mg of intramuscular Depo-Medrone or 40 mg of intralesional Depo-Medrone injection. Blinding proved difficult as the injection sites differed and placebo arms were not included in the study. A Patient-Rated Tennis Elbow Evaluation (PRTEE) Questionnaire and a 10-point Likert scale were used to assess primary outcome. Six weeks after the treatment, there was a reduction in pain, improvement in function and total PRTEE scores in both intramuscular and intralesional groups (p=0.008) using a 95% CI for mean treatment difference of -26 to +16 points. A statistically significant result (p=0.001) in favour of intramuscular causing less pain at the injection site was noted. CONCLUSION: Non-inferiority of intramuscular to intralesional injections was not confirmed; however, the intramuscular injection proved to be effective in reducing tennis elbow-related symptoms and was found less painful at the site of injection at the time of administration. TRIAL REGISTRATION NUMBER: EUDRACT Number: 2010-022131-11. REC Number: 10/H0718/76 (NRES, Central London REC 1).

Computational convergence of the path integral for real dendritic morphologies.

Neurons are characterised by a morphological structure unique amongst biological cells, the core of which is the dendritic tree. The vast number of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of sub-threshold dendritic currents. The Green's function obtained for a given dendritic geometry provides this functional relationship for passive or quasi-active dendrites and can be constructed by a sum-over-trips approach based on a path integral formalism. In this paper, we introduce a number of efficient algorithms for realisation of the sum-over-trips framework and investigate the convergence of these algorithms on different dendritic geometries. We demonstrate that the convergence of the trip sampling methods strongly depends on dendritic morphology as well as the biophysical properties of the cell membrane. For real morphologies, the number of trips to guarantee a small convergence error might become very large and strongly affect computational efficiency. As an alternative, we introduce a highly-efficient matrix method which can be applied to arbitrary branching structures.