Management of dog bite wounds: Our protocol and experience with early surgical intervention

Dog bites injuries are a significant cause of morbidity and mortality. Conventionally, it was suggested to leave the wounds open due to probable increased risk of infections and occurrence of rabies with suturing.Recent publications indicate that primary closure does not necessarily affect the chances of infection but definitely helps in improving the quality of scar. We are presenting our experience and protocol for primary closure of all dog bite wounds. From March 2020 to February 2021, 10 consecutive patients of all ages coming to the emergency of our hospital with category 3 dog bite that penetrated the epidermis and dermis and presenting within 48 hours of injury were included. Every patient was administered first dose of anti rabies vaccine (ARV) (zero dose) for active immunisation and was also given injection tetanus intramuscularly. Mean age of patients in our study was 20.9 with range from 2 years to 90 years. Only 2/10 patients developed infections which were managed conservatively with drainage of abscess and antibiotics. Rest all patients recovered without complications. Primary closure of dog bite wounds when associated with debridement, sufficient irrigation, povidine iodine cleansing and antibiotic administration resulted in improved cosmetic appearance without increase in the rate of infection.

POS0774 ANTI-Sm AUTOANTIBODIES IDENTIFY A PHENOTYPE OF SEVERE SLE WITH AN ASSOCIATED SERUM BIOMARKER PROFILE

Background

Antibodies to Smith (Sm) have been described as one of the most specific autoantibodies for systemic lupus erythematosus (SLE). Other than its association with lupus nephritis, there is, however, limited understanding of its clinical significance1,2.

Objectives

To describe clinical associations and serum protein profiles of anti-Sm positivity in SLE.

Methods

Patients fulfilling SLE classification criteria who were followed longitudinally in a prospective multicentre cohort were studied according to their baseline anti-Sm antibody status. Comparison between Sm+ and Sm- patients was made using descriptive statistics. Clinical associations of Sm positivity with patient disease characteristics were studied using logistic regression. In a subset, 211 serum analytes were measured using Quantibody, Luminex and ELISA assays. Associations between serum proteins and Sm positivity were studied using Least Absolute Shrinkage and Selection Operator (LASSO) penalised regression, adjusting for demographics (age, sex, ethnicity) and medication use

Results

383 patients were studied with median (IQR) follow-up of 4.9 (2,9) years; 65 (17%) had positive anti-Sm antibodies. Sm+ patients were significantly more likely to be of non-European ancestry (OR 2.73, 95% CI 1.55-4.82, p<0.001), and to be positive for anti-dsDNA antibodies (OR 2.8, 95% CI 2.3-3.4, p<0.001), anti-RNP antibodies (OR 15.7, 95% CI 13.9-17.8, p<0.001), direct anti-globulin test (OR 2.36, 95% CI 2.07-2.7, p<0.001) and hypocomplementemia (OR 7.73, 95% CI 5.1-11.7, p<0.001). Sm+ patients were significantly more likely to have active disease during the observation period in a range of organ domains, including mucocutaneous, renal, vasculitis and fever.

More Sm+ patients had episodes of High Disease Activity Status (HDAS, SLEDAI-2K ≧10)3 (OR 3.07, 95% CI 1.70-5.54, p<0.001) and persistent active disease (time-adjusted mean SLEDAI-2K > 4) (OR 3.23. 95% CI 1.84-5.70, p<0.001). Conversely, fewer Sm+ patients attained LLDAS for ≥50% observed time (19.7% vs 41.8%, p=0.002). Sm+ patients were more likely to be treated with glucocorticoids, immunosuppressants, and rituximab. There was no significant difference in damage accrual between Sm + and Sm - patients.

In serum protein analysis (n=197, 29 Sm+), LASSO modelling retained 3 proteins associated with Sm+ status, CXCL13, IL1RL1 and FLT1, along with Asian ethnicity and age. In analysis including pairwise interaction between predictors, 28 Sm+ associated proteins were identified, including CCL4, VCAM1, IL1RL1, Fcg R IIB/C, TDGF1, CEACAM1, TIMP1, BMP5, GDF15, and TNFRSF17.

Conclusion

Anti-Sm autoantibodies, present in 17% of SLE patients, were strongly associated with classical disease manifestations, more severe disease activity, and a specific serological and proteomic profile. These findings suggest anti-Sm+ SLE as a specific disease subset.

References

[1]Barada, FA., B.S. Andrews, J.S. Davis, R.P. Taylor, Antibodies to Sm in patients with systemic lupus erythematosus. Correlation of Sm antibody titers with disease activity and other laboratory parameters. Arthritis Rheum, 1981. 24:1236-1244

[2]Arroyo-Avilla, M, Y. Santiago-Casas, G.McGwin, R.S. Cantor, M. Petri, R. Ramsey-Goldman, J.D. Reveille, R.P.Kimberly, G.S. Alarcon, L.M.Vila, E.E. Brown. Clinical Associations of anti-Smith antibodies in PROFILE: a multi-ethnic lupus cohort. 2015. 34:1217-1223

[3]Koelmeyer, R., H.T. Nim, M. Nikpour, Y.B. Sun, A. Kao, O. Guenther, E. Morand, and A. Hoi, High disease activity status suggests more severe disease and damage accrual in systemic lupus erythematosus. Lupus Sci Med, 2020. 7(1).

Acknowledgements

I would like to acknowledge participants and clinicians involved with the Australian Lupus Registry & Biobank

Disclosure of Interests

None declared

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Background

Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered “data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.

Findings

In our pipeline model, an “interestingness function” assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a “policy” guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.

Conclusions

Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems – and is intended for use with a range of technologies in different deployment scenarios.

MaRe: Processing Big Data with application containers on Apache Spark

BACKGROUND

Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in bioinformatics pipelines. Furthermore, these frameworks do not have native support for application containers, which are becoming popular in scientific data processing.

RESULTS

Here we present MaRe, an open source programming library that introduces support for Docker containers in Apache Spark. Apache Spark and Docker are the MapReduce framework and container engine that have collected the largest open source community; thus, MaRe provides interoperability with the cutting-edge software ecosystem. We demonstrate MaRe on 2 data-intensive applications in life science, showing ease of use and scalability.

CONCLUSIONS

MaRe enables scalable data-intensive processing in life science with Apache Spark and application containers. When compared with current best practices, which involve the use of workflow systems, MaRe has the advantage of providing data locality, ingestion from heterogeneous storage systems, and interactive processing. MaRe is generally applicable and available as open source software.

On-demand virtual research environments using microservices

The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.

Efficient iterative virtual screening with Apache Spark and conformal prediction

Background

Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands.

Contribution

In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as ‘low-scoring’ ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling.

Results

We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.

A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits

In quantitative trait locus (QTL) mapping significance of putative QTL is often determined using permutation testing. The computational needs to calculate the significance level are immense, 10⁴ up to 10⁸ or even more permutations can be needed. We have previously introduced the PruneDIRECT algorithm for multiple QTL scan with epistatic interactions. This algorithm has specific strengths for permutation testing. Here, we present a flexible, parallel computing framework for identifying multiple interacting QTL using the PruneDIRECT algorithm which uses the map-reduce model as implemented in Hadoop. The framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way. Our work underlines the maturity of accessing distributed parallel computing for computationally demanding bioinformatics applications through building workflows within existing scientific environments. We investigate the PruneDIRECT algorithm, comparing its performance to exhaustive search and DIRECT algorithm using our framework on a public cloud resource. We find that PruneDIRECT is vastly superior for permutation testing, and perform 2 ×10⁵ permutations for a 2D QTL problem in 15 hours, using 100 cloud processes. We show that our framework scales out almost linearly for a 3D QTL search.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Familial neck-tongue syndrome

Neck-tongue syndrome is an uncommon clinical entity characterized by brief attacks of intense unilateral stabbing pain in the upper neck or occipital region upon sudden rotation of the head, accompanied by ipsilateral numbness of the tongue. Eight patients, 5 teenagers and 3 adults, with neck-tongue syndrome are presented. Each of the 5 adolescents had normal examinations and normal neuroimaging. The 3 adults were parents of the affected children and had experienced transient symptoms during their adolescence suggesting an autosomal dominant inheritance pattern.

Children's ibuprofen suspension for the acute treatment of pediatric migraine

OBJECTIVE

To compare the efficacy of a single over-the-counter dose (7.5 mg/kg, p.o.) of children's ibuprofen suspension vs. placebo for the acute treatment of pediatric migraine.

BACKGROUND

Migraine occurs in 4% of young children. There is a paucity of controlled clinical research in the treatment of childhood migraine and there are currently no approved drugs in the USA for treatment of migraine in children < or = 12 years of age. The purpose of this study is to assess the efficacy and tolerability of a single OTC dose of ibuprofen suspension for the acute treatment of childhood migraine.

METHODS

Prospective, double-blind, placebo-controlled, parallel group, randomized study of children 6-12 yrs with migraine (I.H.S.-R 1997) treating 1 attack with a 7.5 mg/kg liq. ibuprofen vs matching placebo. Efficacy measures: (1). Headache severity based upon a 4 pt scale (severe, mod., mild, no headache) at 30, 60, 90, 120, 180 and 240 minutes post dose, and (2). nausea, vomiting, and photo/phonophobia at 120 min. The 1 degrees endpoint was cumulative % of responders (severe or mod. headache reduced to mild or none) by 120 minutes. Secondary endpoints were headache recurrence within 4-24 hours and need for rescue medicines within 4 hours.

RESULTS

138 enrolled; 84 treated/completed diary. 45 active agent, 39 placebo. The 2 groups were comparable (active: placebo) - Ages: 9: 9.1, gender boy/girl - 1.25: 1.6, and diagnosis: migraine w/o aura - 86%: 79%. Concomitant use of prophylactic Rx: 24%: 10% (Table 3). Nausea was eliminated in 60% of the ibuprofen treated patients and 39% of the placebo group (p<0.001). Vomiting, photophobia and phonophobia had marginal, but not statistically significant, decreases at 2 hours. A striking gender difference was noted (Table 4): No AE's were reported.

CONCLUSION

Children's ibuprofen suspension at an OTC dose of 7.5 mg/kg is an effective and well-tolerated agent for pain relief in the acute treatment of childhood migraine, particularly in boys. There is a striking difference in gender response rates and placebo responder rates between girls and boys. The boys responded at a statistically significant rate, and girls failed to do so because of a very high placebo responder rate. Multi-center trials are recommended.