Protection of pigs against experimental Salmonella Typhimurium infection by use of a single dose subunit slow delivery vaccine

Infections caused by septicemic strains of Salmonella are significant animal health as well as food safety concerns for the North American swine industry. Among the various strategies to control these infections at the herd level, development of vaccines are attractive alternatives. In this study, based on previous studies of immune response to various protems following natural and experimental infections of pigs by Salmonella, we designed a subunit slow delivery vaccine and tested it in an experimental model of infection. The selected immunogenic protein was cloned and purified by chromatography. The purified protein was then incorporated m PLGA (a polymer that is slowly degraded within the animal\u27s gastro-intestinal system) microspheres and given orally once to groups of pigs (n=8) while control animals (n=8) received only PBS. Animals were challenged orally 4 weeks after the vaccmation with 108 cells of a virulent strains of Salmonella Typhimurium. Animals were examined twice a day and climcal signs evaluated using a predetermined scoring grid. Pigs were sacrificed 12 days later and bacterial cultures of vanous organs, electron microscopy and evaluation of lgA response by ELISA were performed. No significant difference was found at bacteriology and ELISA but marked differences in clinical signs were observed between vaccinated and non vaccinated animals. None of vaccmated animals showed fever exceeding 40°C while it was observed in 5 out of 8 non vaccinated Only one of vaccmated pigs showed mild diarrhea while severe diarrhea was observed in all control animals different sizes of microspheres were observed in intestinal crypts of vaccinated animals at electron microscopy. We concluded that this vaccine can protect pigs against clinical signs associated with experimental infection by Salmonella Typhimunum

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Computationally restoring the potency of a clinical antibody against Omicron

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drug

An Active Learning Algorithm for Control of Epidural Electrostimulation

Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm’s performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions’ results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy

Prediction of early unplanned intensive care unit readmission in a UK tertiary-care hospital: A cross-sectional machine learning approach

Objectives: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult, and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. Setting: A single academic, tertiary care hospital in the United Kingdom. Participants: A set of 3,326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who: were ≤ 16 years of age; visited intensive care units other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2,018 outcome-labeled episodes remained. Primary and Secondary Outcome Measures: Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge. Results: In ten-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the MIMIC-III database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built SWIFT score (AUROC = 0.6082, SE 0.0249; p = 0.014, pairwise t-test). Conclusions: Despite the inherent difficulties, we demonstrate that a novel ML algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.esearch reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945

Comparison of bioinspired algorithms applied to cancer database

Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database

Magnetic-field control of topological electronic response near room temperature in correlated Kagome magnets

Strongly correlated Kagome magnets are promising candidates for achieving controllable topological devices owing to the rich interplay between inherent Dirac fermions and correlation-driven magnetism. Here we report tunable local magnetism and its intriguing control of topological electronic response near room temperature in the Kagome magnet Fe3Sn2 using small angle neutron scattering, muon spin rotation, and magnetoresistivity measurement techniques. The average bulk spin direction and magnetic domain texture can be tuned effectively by small magnetic fields. Magnetoresistivity, in response, exhibits a measurable degree of anisotropic weak localization behavior, which allows the direct control of Dirac fermions with strong electron correlations. Our work points to a novel platform for manipulating emergent phenomena in strongly-correlated topological materials relevant to future applications