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Background and ObjectivesPatients with pediatric-onset multiple sclerosis (POMS) typically experience higher levels of inflammation with more frequent relapses and reach irreversible disability at a younger age than adult-onset patients. There have been few randomized placebo-controlled clinical trials of multiple sclerosis (MS) disease-modifying therapies (DMTs) in patients with POMS, and most available data are based on observational studies of off-label use of DMTs approved for adults. We assessed the effectiveness of natalizumab compared with fingolimod using injectable platform therapies as a reference in pediatric patients in the global MSBase registry.MethodsThis retrospective study included patients with POMS who initiated treatment with an injectable DMT, natalizumab, or fingolimod between January 1, 2006, and May 3, 2021 (N=1218). The primary outcome was the time to first relapse from index therapy initiation. Secondary study outcomes included annualized relapse rate; proportions of relapse-free patients at 1, 2, and 5 years post baseline; time to treatment discontinuation; and times to 24-week confirmed disability worsening and confirmed disability improvement.ResultsPatients treated with fingolimod had a significantly lower risk of relapse than patients treated with injectable DMT (hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.29–0.83;P=0.008). After adjustment for prior DMT experience in the unmatched sample, patients treated with natalizumab had a significantly lower risk of relapse than patients treated either with injectable DMT (HR, 0.15; 95% CI, 0.07–0.31;PP=0.049). The adjusted secondary study outcomes were generally consistent with the primary outcome or with previous observations. The findings in the inverse probability treatment weighting–adjusted patient populations were confirmed in multiple sensitivity analyses.DiscussionOur results suggest that natalizumab and fingolimod have broadly equivalent therapeutic efficacies in patients with POMS, consistent with previous studies of natalizumab and fingolimod in adult-onset patients and POMS. However, analyses of relapse outcomes suggest natalizumab is superior to fingolimod in the control of relapses in this population with high rates of new inflammatory activity.Classification of EvidenceThis study provides Class III evidence that natalizumab may provide better disease control than fingolimod in patients with POMS.

Background: While farm animal veterinarians receive extensive training in the diagnosis and treatment of individual animals, population medicine or ‘herd health’ has also become commonplace in modern production animal veterinary medicine. Clinicians are increasingly using routinely collected farm data to identify epidemiological patterns in order to implement preventive health management changes on farm. As such, data handling and statistical analysis techniques are becoming more important in farm practice. While data handling and statistical techniques can initially be challenging to learn, a relatively small investment in time spent learning can dramatically reduce the time spent analysing farm outcomes, and provide a useful evidence base on which to base on-farm management decisions. Aim of the article: This article provides a practical guide to data skills for the production animal vet, briefly covering basic descriptive statistics and graphical representations of data. It also includes further examples of more advanced data analysis techniques, such as statistical modelling and the use of coding language.

BackgroundOver the decades, several natural history studies on patients with primary (PPMS) or secondary progressive multiple sclerosis (SPMS) were reported from international registries. In PPMS, a consistent heterogeneity on long-term disability trajectories was demonstrated. The aim of this study was to identify subgroups of patients with SPMS with similar longitudinal trajectories of disability over time.MethodsAll patients with MS collected within Big MS registries who received an SPMS diagnosis from physicians (cohort 1) or satisfied the Lorscheider criteria (cohort 2) were considered. Longitudinal Expanded Disability Status Scale (EDSS) scores were modelled by a latent class growth analysis (LCGA), using a non-linear function of time from the first EDSS visit in the range 3–4.ResultsA total of 3613 patients with SPMS were included in the cohort 1. LCGA detected three different subgroups of patients with a mild (n=1297; 35.9%), a moderate (n=1936; 53.6%) and a severe (n=380; 10.5%) disability trajectory. Median time to EDSS 6 was 12.1, 5.0 and 1.7 years, for the three groups, respectively; the probability to reach EDSS 6 at 8 years was 14.4%, 78.4% and 98.3%, respectively. Similar results were found among 7613 patients satisfying the Lorscheider criteria.ConclusionsContrary to previous interpretations, patients with SPMS progress at greatly different rates. Our identification of distinct trajectories can guide better patient selection in future phase 3 SPMS clinical trials. Additionally, distinct trajectories could reflect heterogeneous pathological mechanisms of progression.

Identification of variables truly associated with an outcome, whilst minimising false positives,is critical to any data analysis. This is particularly challenging in wider datasets, where the proportionof false positives tends to increase as the number of variables increases. Whilst stability selection hasbeen demonstrated to improve model performance, arbitrary stability thresholds mean performanceimprovements are model and dataset dependent. We propose a novel permutation-based objectivestability threshold (POST) to enhance the stability selection procedure. The performance of POSTis evaluated through simulation analysis, and performance benefits of POST are demonstrated incomparison with conventional approaches. Furthermore, the advantages of statistical triangulation,the use of multiple models simultaneously, alongside POST are highlighted. To facilitate the implementationof these methods, we present the R package stabiliser, which is designed to improve inferentialmodel performance regardless of model type or dataset structure.

Inferential research commonly involves identification of causal factors from within high dimensional data but selection of the ‘correct’ variables can be problematic. One specific problem is that results vary depending on statistical method employed and it has been argued that triangulation of multiple methods is advantageous to safely identify the correct, important variables. To date, no formal method of triangulation has been reported that incorporates both model stability and coefficient estimates; in this paper we develop an adaptable, straightforward method to achieve this. Six methods of variable selection were evaluated using simulated datasets of different dimensions with known underlying relationships. We used a bootstrap methodology to combine stability matrices across methods and estimate aggregated coefficient distributions. Novel graphical approaches provided a transparent route to visualise and compare results between methods. The proposed aggregated method provides a flexible route to formally triangulate results across any chosen number of variable selection methods and provides a combined result that incorporates uncertainty arising from between-method variability. In these simulated datasets, the combined method generally performed as well or better than the individual methods, with low error rates and clearer demarcation of the true causal variables than for the individual methods.

Background Since 2010, 27 mixed-treatment comparisons (MTCs) of disease-modifying therapies (DMTs) for multiple sclerosis have been published. However, there has been continued evolution in the field of MTCs. Additionally, limitations in methodological approach and reporting transparency, even in the most recent publications, makes interpretation and comparison of existing studies difficult. Objectives The objectives of this study are twofold: (1) to estimate the efficacy and safety of DMTs at European Commission-approved doses compared with placebo in adults with relapsing–remitting multiple sclerosis (RRMS) using MTC, and (2) to identify and address methodological challenges when performing MTC in RRMS, thereby creating a baseline for comparisons with future treatments. Methods Searches were completed in 14 databases, including MEDLINE, Embase, CENTRAL, CDSR and DARE, from inception to June 2018 to identify published or unpublished prospective, randomised controlled trials of all European Union-approved DMTs or DMTs expected to be approved in the near future in RRMS or rapidly-evolving severe RRMS. No language or date restrictions were applied. Studies were included in the MTC if they were judged to have sufficiently similar characteristics, based on the following: patient age; proportion of male participants; Expanded Disability Status Scale (EDSS) score; duration of disease; number of relapses prior to enrolment and proportion of previously treated patients. Background information from the included studies, as well as effect size and confidence intervals (where relevant) of defined outcomes were extracted. Reporting of the MTC was consistent with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) network meta-analysis guidelines. Results In total, 33 studies were included in the MTC. Annualised relapse rate (ARR 28 trials) was significantly reduced in all treatments compared with placebo. Alemtuzumab had the highest probability (63%) of being the most effective treatment in terms of ARR compared with placebo (rate ratio [RR] 0.28, 95% credible interval [CrI] 0.21–0.38), followed by natalizumab (30% probability; RR 0.32, 95% CrI 0.23–0.43). The risk of 3- and 6-month confirmed disability progression (CDP3M, 13 trials; CDP6M, 14 trials) were similar; CDP6M was significantly reduced for alemtuzumab (hazard ratio [HR] 0.365; 95% CrI 0.165–0.725), ocrelizumab (HR 0.405, 95% CrI 0.188–0.853) and natalizumab (HR 0.459, 95% CrI 0.252–0.840) relative to placebo. There were no significant differences in the odds of serious adverse events (SAEs, 6 trials) between any treatment and placebo. The results of the MTC were limited by the lack of studies reporting direct comparisons between the included treatments and by heterogeneous reporting of key outcome data. Conclusions Meta-analyses confirmed the benefit of all DMTs in terms of relapse rate compared with placebo with a comparable rate of SAEs for the DMTs that could be included in the network. The rigor and transparency of reporting in this study provide a benchmark for comparisons with future new agents. Electronic Supplementary Material The online version of this article (10.1007/s40120-020-00212-5) contains supplementary material, which is available to authorized users.

Group III monochalcogenides such as GaSe and GaS have attracted considerable interest as two-dimensional (2D) alternatives to the traditional transition metal dichalcogenides. The production of large-area films as well as the long-term ambient stability remains a challenge for scalable integration of these materials into the next generation of 2D circuitry and optoelectronic devices. In this report, a simple atmospheric-pressure chemical vapor deposition method is proposed to synthesize continuous monolayers of GaSe and GaS. The proposed method utilizes commercially available precursors and does not requires vacuum-sealed ampules or exfoliation. The optimal parameters for continuous monolayer self-limited growth were determined by systematically changing the growth time, the gas flow rate, and the amount of precursors. So far, the study of bare monolayer GaSe by Raman spectroscopy has been difficult due to the very low Raman signal and a rapid laser-induced oxidation of the material. A laser-scanning method that minimizes the cumulative laser damage and allows a reasonable signal-to-noise ratio was utilized to study the time-dependent ambient stability of bare and encapsulated monolayer samples by Raman spectroscopy. Our results reveal that bare GaSe monolayers can stand up to 6 h in air before complete degradation, and encapsulation with transparent polymeric films can delay this process for few days. These results open the door to produce large-area films of monolayer group III monochalcogenides and to consider film encapsulation with different transparent polymers that could further extend the long-term durability of these ambient sensitive 2D materials.

National bodies in Great Britain (GB) have expressed concern over young stock health and welfare and identified calf survival as a priority; however, no national data have been available to quantify mortality rates. The aim of this study was to quantify the temporal incidence rate, distributional features, and factors affecting variation in mortality rates in calves in GB since 2011. The purpose was to provide information to national stakeholder groups to inform resource allocation both for knowledge exchange and future research. Cattle birth and death registrations from the national British Cattle Movement Service were analyzed to determine rates of both slaughter and on-farm mortality. The number of births and deaths registered between 2011 and 2018 within GB were 21.2 and 21.6 million, respectively. Of the 3.3 million on-farm deaths, 1.8 million occurred before 24 mo of age (54%) and 818,845 (25%) happened within the first 3 mo of age. The on-farm mortality rate was 3.87% by 3 mo of age, remained relatively stable over time, and was higher for male calves (4.32%) than female calves (3.45%). Dairy calves experience higher on farm mortality rates than nondairy (beef) calves in the first 3 mo of life, with 6.00 and 2.86% mortality rates, respectively. The 0- to 3-mo death rate at slaughterhouse for male dairy calves has increased from 17.40% in 2011 to 26.16% in 2018, and has remained low (

International audience; On 12 September 2019, the global Patient Reported Outcome for Multiple Sclerosis (PROMS) Initiative was launched at the 35th Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). The multi-stakeholder PROMS Initiative is jointly led by the European Charcot Foundation (ECF) and the Multiple Sclerosis International Federation (MSIF), with the Italian Multiple Sclerosis Society (AISM) acting as the lead agency for and on behalf of the global MSIF movement. The initiative has the ambitious mission to (i) maximize the impact of science with and of patient input on the life of people affected by MS, and (ii) to represent a unified view on Patient-Reported Outcomes for MS to people affected by MS, healthcare providers, regulatory agencies and Health Technologies Assessments agencies. Equipped with an innovative participatory governance of an international and interdisciplinary network of different stakeholders, PROMS has the potential to guide future breakthroughs in MS patient-focused research and care. In this paper we present the progresses of the global PROMS Initiative and discuss the open questions that we aim to address.

BACKGROUND Bovine respiratory disease (BRD) is common in cattle youngstock on UK dairy farms. The current diagnostic methods lack sensitivity and specificity. Thoracic ultrasound (TUS) is a relatively new BRD diagnostic tool with increased accuracy. METHOD Data were collected from TUS examinations of 347 calves on Scottish dairy farms and compared to farmer BRD treatment rates. A calf health scoring tool was used to collect information on youngstock health and management on these farms. RESULTS Of 347 ultrasonographic examinations, 53 (15.3%) were classified as abnormal and 294 (84.7%) as normal. Of the 53 calves classified as abnormal, only 13 (24.5%) were treated by the farmer; however, of the 294 classified as normal, 22 (7.5%) were treated. CONCLUSION The results indicated farmers were misdiagnosing BRD in youngstock. Use of TUS on UK farms may improve diagnostic rates and accuracy, reduce the unnecessary use of antimicrobials and demonstrate the value of preventive healthcare programmes.

Covariate selection when the number of available variables is large relative to the number of observations is problematic in epidemiology and remains the focus of continued research. Whilst a variety of statistical methods have been developed to attempt to overcome this issue, at present very few methods are available for wide data that include a clustered outcome. The purpose of this research was to make an empirical evaluation of a new method for covariate selection in wide data settings when the dependent variable is clustered. We used 3300 simulated datasets with a variety of defined structures and known sets of true predictor variables to conduct an empirical evaluation of a mixed model stability selection procedure. Comparison was made with an alternative method based on regularisation using the least absolute shrinkage and selection operator (Lasso) penalty. Model performance was assessed using several metrics including the true positive rate (proportion of true covariates selected in a final model) and false discovery rate (proportion of variables selected in a final model that were non-true (false) variables). For stability selection, the false discovery rate was consistently low, generally remaining ≤ 0.02 indicating that on average fewer than 1 in 50 of the variables selected in a final model were false variables. This was in contrast to the Lasso-based method in which the false discovery rate was between 0.59 and 0.72, indicating that generally more than 60% of variables selected in a final model were false variables. In contrast however, the Lasso method attained higher true positive rates than stability selection, although both methods achieved good results. For the Lasso method, true positive rates remained ≥ 0.93 whereas for stability selection the true positive rate was 0.73–0.97. Our results suggest both methods may be of value for covariate selection with high dimensional data with a clustered outcome. When high specificity is needed for identification of true covariates, stability selection appeared to offer the better solution, although with a slight loss of sensitivity. Conversely when high sensitivity is needed, the Lasso approach may be useful, even if accompanied by a substantial loss of specificity. Overall, the results indicated the loss of sensitivity when employing stability selection is relatively small compared to the loss of specificity when using the Lasso and therefore stability selection may provide the better option for the analyst when evaluating data of this type.

Neonatal calves are relatively susceptible to heat loss, and previous research suggests that reduced environmental temperatures are associated with reduced average daily gain (ADG) during the preweaning phase. Current methods of mitigating negative effects of colder environmental conditions include the use of calf jackets and the provision of supplementary heat sources; however, previous research is limited. The aim of this study was to evaluate the effect of calf jackets and 1-kW heat lamps on the growth rates of preweaning calves and evaluate associations between environmental temperature and ADG using a Bayesian approach to incorporate both current and previous data. Seventy-nine calves from a single British dairy farm were randomly allocated at birth to 1 of the following 4 groups: no jacket and no heat lamp, heat lamp but no jacket, jacket but no heat lamp, or both heat lamp and jacket between January and April of 2021. Calves were weighed at both birth and at approximately 21 d of age. Temperature was recorded both inside and outside of the calf building, and in pens both with and without heat lamps using data loggers. To explore the effect of treatment group and environmental temperature on ADG, a fixed effects model was fitted over 1,000 bootstrap samples. The effect of environmental temperature on ADG was further explored within a Bayesian framework that used temperature and ADG data for 484 calves from 16 farms available from a previous trial as prior information. Calves housed under a 1-kW heat lamp had an increased ADG of 0.09 kg/d (95% bootstrap confidence interval: -0.02 to 0.20 kg/d), and no effect of jacket or interactions between jacket and heat lamp were found. A significant positive association was identified between the mean environmental temperature of the calf building and ADG, with a 1°C increase in temperature being associated with a 0.03 kg/d increase in ADG (95% bootstrap confidence interval: 0.01 to 0.04 kg/d). Associations between environmental temperature and ADG were further evaluated within a Bayesian framework, and posterior estimates were 0.014 kg/d of ADG per 1°C increase (95% credible interval: 0.009 to 0.021 kg/d). This study demonstrated that a 1-kW heat lamp was effective in increasing ADG in calves, and no significant effect of calf jacket on ADG was found. A significant, positive effect of increased pen temperature on calf ADG was identified in this study and was reinforced when including prior information from previous research within a Bayesian framework.

Background Heart girth tapes (HGTs) are often used as an alternative to weight scales for calves. This study investigated the accuracy of HGT in estimating bodyweight and daily liveweight gain (DWLG) of pre‐weaned calves, and the impact of inter‐observer variation. Method In Study 1, 119 calves were weighed using HGT and electronic scales on multiple occasions. Mixed‐effects models for both bodyweight and DLWG were used to determine the accuracy of HGT compared to the electronic scales. Simulation data were used to further analyse the accuracy of DLWG estimation including for factors such as the effect of group size on group DLWG estimates. In Study 2, 10 observers weighed 20 pre‐weaned calves, using HGT and electronic scales. Mixed‐effect model was used to investigate the impact of different observers on the accuracy of HGT on measuring bodyweights. Results Mixed‐effects model results suggest HGT provides a relatively accurate estimation of weight (MAE: 2.66 kg) and relatively inaccurate estimation of DLWG (MAE 0.10 kg/d). Simulated data identified associations between time between weight dates and error in DLWG estimation, with MAE of individual DLWG estimation decreasing from 0.43 kg/d when 14 days apart to 0.08 kg/d when 70 days apart. Increased calf numbers reduced error rates of group DLWG estimation, with 90% of simulations when 12 calves were weighed 70 days apart. Conclusions HGTs are relatively accurate at estimating individual bodyweights but are unreliable methods for measuring DLWG in individual calves, particularly weighed within a short‐time period. Estimates at group level however are relatively accurate, providing there is a suitable period of time between weigh dates and an appropriate number of calves per group.

Epidemiological research commonly involves identification of causal factors from within high dimensional (wide) data, where predictor variables outnumber observations. In this situation, however, conventional stepwise selection procedures perform poorly. Selection stability is one method to aid robust variable selection, by refitting a model to repeated resamples of the data and calculating the proportion of times each covariate is selected. A key problem when applying selection stability is to determine a threshold of stability above which a covariate is deemed ‘important’. In this research we describe and illustrate a two-step process to implement a stability threshold for covariate selection. Firstly, covariate stability distributions were established with a permuted model (randomly reordering the outcome to sever the relationship with predictors) using a cumulative distribution function. Subsequently, covariate stability was estimated using the true model outcome and covariates with a stability above a threshold defined from the permuted model, were selected in a final model. The proposed method performed well across 22 varied, simulated datasets with known outcomes; selection error rates were consistently lower than conventional implementation of equivalent models. This method of covariate selection appears to offer substantial advantages over current methods, to accurately identify the correct covariates from within a large, complex parameter space.

Background: There has been much scrutiny of antimicrobial use in the livestock sector, since the O’Neill report, a 2015 government -commissioned review of antimicrobial resistance, called for the agriculture industry to reduce its use of antimicrobials. Aim of the article: This article outlines key herd health approaches to common diseases on dairy farms that will result in a decrease in antimicrobial use, with a focus on those interventions that are most likely to result in the biggest reductions.

Background: Stratified medicine methodologies based on subgroup analyses are often insufficiently powered. More powerful personalized medicine approaches are based on continuous scores. Objective: We deployed a patient-specific continuous score predicting treatment response in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: Data from two independent randomized controlled trials (RCTs) were used to build and validate an individual treatment response (ITR) score, regressing annualized relapse rates (ARRs) on a set of baseline predictors. Results: The ITR score for the combined treatment groups versus placebo detected differential clinical response in both RCTs. High responders in one RCT had a cross-validated ARR ratio of 0.29 (95% confidence interval (CI) = 0.13–0.55) versus 0.62 (95% CI = 0.47–0.83) for all other responders (heterogeneity p = 0.038) and were validated in the other RCT, with the corresponding ARR ratios of 0.31 (95% CI = 0.18–0.56) and 0.61 (95% CI = 0.47–0.79; heterogeneity p = 0.036). The strongest treatment effect modifiers were the Short Form-36 Physical Component Summary, age, Visual Function Test 2.5%, prior MS treatment and Expanded Disability Status Scale. Conclusion: Our modelling strategy detects and validates an ITR score and opens up avenues for building treatment response calculators that are also applicable in routine clinical practice.

Background: The optimal timing of treatment starts for achieving the best control on the long-term disability accumulation in multiple sclerosis (MS) is still to be defined. Objective: The aim of this study was to estimate the optimal time to start disease-modifying therapies (DMTs) to prevent the long-term disability accumulation in MS, using a pooled dataset from the Big Multiple Sclerosis Data (BMSD) network. Methods: Multivariable Cox regression models adjusted for the time to first treatment start from disease onset (in quintiles) were used. To mitigate the impact of potential biases, a set of pairwise propensity score (PS)-matched analyses were performed. The first quintile, including patients treated within 1.2 years from onset, was used as reference. Results: A cohort of 11,871 patients (median follow-up after treatment start: 13.2 years) was analyzed. A 3- and 12-month confirmed disability worsening event and irreversible Expanded Disability Status Scale (EDSS) 4.0 and 6.0 scores were reached by 7062 (59.5%), 4138 (34.9%), 3209 (31.1%), and 1909 (16.5%) patients, respectively. The risk of reaching all the disability outcomes was significantly lower ( p < 0.0004) for the first quintile patients’ group. Conclusion: Real-world data from the BMSD demonstrate that DMTs should be commenced within 1.2 years from the disease onset to reduce the risk of disability accumulation over the long term.

There is increasing emphasis on the need to reduce antimicrobial use (AMU) on dairy farms to reduce the emergence of resistant bacteria which could compromise animal health and impact human medicine. In addition to AMU, the role of farm management is an area of growing interest and represents an alternative route for possible interventions. The aim of this study was to evaluate the impact of farm management practices and AMU on resistances of sentinel bacteria in bulk milk. Dairy farms from two, geographically separate locations within the British Isles were recruited as part of two study groups. Farm management data from study group 1 (n = 125) and study group 2 (n = 16) were collected by means of a face-to-face questionnaire with farmers carried out during farm visits. For study group 2, additional data on AMU was collated from veterinary medicine sales records. Sentinel bacterial species (Enterococcus spp. and E. coli), which have been reported to be of value in antimicrobial resistance (AMR) studies, were isolated from bulk tank milk to monitor antimicrobial susceptibilities by means of minimum inhibitory concentrations (MICs). MIC data for both groups was used to generate an overall “score” for each farm. For both groups, this overall farm mean MIC was used as the outcome variable to evaluate the impact of farm management and AMU. This was achieved through use of elastic net modelling, a regularised regression method which also featured a bootstrapping procedure to produce robust models. Inference of models was based on covariate stabilities and bootstrapped P-values to identify farm management and AMU practices that have significant effects on MICs of sentinel bacteria. Practices which were found to be of importance with respect to Enterococcus spp. included management of slurry, external entry of livestock to the dairy herd, use of bedding materials and conditioners, cubicle cleaning routines and antibiotic practices, including use of β-lactams and fluoroquinolones. Practices deemed to be of importance for E. coli MICs included cubicle and bedding management practices, teat preparation routines at milking and the milking procedure itself. We conclude that a variety of routine farm management practices are associated with MICs of sentinel bacteria in bulk milk. Amendment of these practices offers additional possible routes of intervention, alongside alterations to AMU, to mitigate the emergence and dissemination of AMR on dairy farms.

© Copyright © 2020 Hyde, Green, Hudson and Down. Total bacterial counts (TBC) and coliform counts (CC) were estimated for 328 colostrum samples from 56 British dairy farms. Samples collected directly from cows' teats had lower mean TBC (32,079) and CC (21) than those collected from both colostrum collection buckets (TBC: 327,879, CC: 13,294) and feeding equipment (TBC: 439,438, CC: 17,859). Mixed effects models were built using an automated backwards stepwise process in conjunction with repeated bootstrap sampling to provide robust estimates of both effect size and 95% bootstrap confidence intervals (BCI) as well as an estimate of the reproducibility of a variable effect within a target population (stability). Colostrum collected using parlor (2.06 log cfu/ml, 95% BCI: 0.35–3.71) or robot (3.38 log cfu/ml, 95% BCI: 1.29–5.80) milking systems, and samples collected from feeding equipment (2.36 log cfu/ml, 95% BCI: 0.77–5.45) were associated with higher TBC than those collected from the teat, suggesting interventions to reduce bacterial contamination should focus on the hygiene of collection and feeding equipment. The use of hot water to clean feeding equipment (−2.54 log cfu/ml, 95% BCI: −3.76 to −1.74) was associated with reductions in TBC, and the use of peracetic acid (−2.04 log cfu/ml, 95% BCI: −3.49 to −0.56) or hypochlorite (−1.60 log cfu/ml, 95% BCI: −3.01 to 0.27) to clean collection equipment was associated with reductions in TBC compared with water. Cleaning collection equipment less frequently than every use (1.75 log cfu/ml, 95% BCI: 1.30–2.49) was associated with increased TBC, the use of pre-milking teat disinfection prior to colostrum collection (−1.85 log cfu/ml, 95% BCI: −3.39 to 2.23) and the pasteurization of colostrum (−3.79 log cfu/ml, 95% BCI: −5.87 to −2.93) were associated with reduced TBC. Colostrum collection protocols should include the cleaning of colostrum collection and feeding equipment after every use with hot water as opposed to cold water, and hypochlorite or peracetic acid as opposed to water or parlor wash. Cows' teats should be prepared with a pre-milking teat disinfectant and wiped with a clean, dry paper towel prior to colostrum collection, and colostrum should be pasteurized where possible.

IntroductionThe approved dosing of natalizumab (300 mg infusion every 4 weeks [Q4W]) is associated with risk of progressive multifocal leukoencephalopathy. This analysis from the TYSABRI Observational Program (TOP) compared relapse outcomes in patients who switched to natalizumab every 6 weeks (Q6W) after 1 year of Q4W with patients who remained on Q4W.MethodsEligible TOP patients had ≥1 year of natalizumab Q4W. Treatment duration-matched Q6W and Q4W patients (191 pairs) were compared by propensity score matching (PSM). Negative binomial model-estimated annualised relapse rate (ARR) and Kaplan-Meier-estimated relapse risk were compared during the post-switch and exposure-matched follow-up periods.ResultsAfter PSM, 135 pairs of Q6W and Q4W patients were included. Mean treatment duration was4.4 years in both groups. There was no difference in ARR (Q6W: 0.231 [95% confidence interval (CI), 0.145–0.367]; Q4W: 0.254 [0.160–0.402]; P=0.774) or relapse risk (hazard ratio [95% CI], 1.021 [0.583–1.789]; P=0.940) between groups.ConclusionsNo significant difference in relapse outcomes was observed between patients who switched4t6o Q6W and those who remained on Q4W. An ongoing randomized prospective trial of Q4W vs Q6W will inform further on the effectiveness of natalizumab Q6W. Support: Biogen.peiran.ho@biogen.com

Background The COVID-19 pandemic has reinforced the importance of research for the health of our society and highlighted the need for stakeholders of the health research and care continuum to form a collaborative and interdependent ecosystem. Objective With the world still reeling from waves of the COVID-19 pandemic and adapting to the vaccine rollout at widely different rates, the International Progressive MS Alliance (hereafter Alliance) organized a meeting (April 2021) to consider how the Covid-19 pandemic impacts the health and well-being of people with progressive Multiple Sclerosis (MS). Methods We invited the Alliance stakeholders and experts to present what they have learned about SARS-CoV-2 infection and progressive MS and to define future scientific priorities. Results The meeting highlighted three priorities for additional focus: (1) the impact of Disease Modifying Therapies (DMTs) on the risk of COVID-19 and on the efficacy of COVID-19 vaccines in people with progressive MS; (2) the long-term impact of COVID-19 and COVID-19 vaccines on the biology of progressive MS; and (3) the impact on well-being of people with progressive MS. Conclusion This paper's calls to action could represent a path toward a shared research agenda. Multi-stakeholder and long-term investigations will be required to drive and evolve such an agenda.

The preweaning period is vital in the development of calves on dairy farms and improving daily liveweight gain (DLWG) is important to both financial and carbon efficiency; minimising rearing costs and improving first lactation milk yields. In order to improve DLWG, veterinary advisors should provide advice that has both a large effect size as well as being consistently important on the majority of farms. Whilst a variety of factors have previously been identified as influencing the DLWG of preweaned calves, it can be challenging to determine their relative importance, which is essential for optimal on-farm management decisions. Regularised regression methods such as ridge or lasso regression provide a solution by penalising variable coefficients unless there is a proportional improvement in model performance. Elastic net regression incorporates both lasso and ridge penalties and was used in this research to provide a sparse model to accommodate strongly correlated predictors and provide robust coefficient estimates. Sixty randomly selected British dairy farms were enrolled to collect weigh tape data from preweaned calves at birth and weaning, resulting in data being available for 1014 calves from 30 farms after filtering to remove poor quality data, with a mean DLWG of 0.79 kg/d (range 0.49–1.06 kg/d, SD 0.13). Farm management practices (e.g. colostrum , feeding, hygiene protocols), building dimensions, temperature/humidity and colostrum quality/bacteriology data were collected, resulting in 293 potential variables affecting farm level DLWG. Bootstrapped elastic net regression models identified 17 variables as having both a large effect size and high stability. Increasing the maximum preweaned age within the first housing group (0.001 kg/d per 1d increase, 90 % bootstrap confidence interval (BCI): 0.000−0.002), increased mean environmental temperature within the first month of life (0.012 kg/d per 1 °C increase, 90 % BCI: 0.002−0.037) and increased mean volume of milk feeding (0.012 kg/d per 1 L increase, 90 % BCI: 0.001−0.024) were associated with increased DLWG. An increase in the number of days between the cleaning out of calving pen (-0.001 kg/d per 1d increase, 90 % BCI: -0.001−0.000) and group housing pens (-0.001 kg/d per 1d increase, 90 % BCI: -0.002−0.000) were both associated with decreased DLWG. Through bootstrapped elastic net regression, a small number of stable variables have been identified as most likely to have the largest effect size on DLWG in preweaned calves. Many of these variables represent practical aspects of management with a focus around stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature; these variables should now be tested in a randomised controlled trial to elucidate causality.

Background Magnetic resonance imaging (MRI) data from multiple sclerosis (MS) patients treated in real-world settings are important for understanding disease-modifying therapy effects, including no evidence of disease activity (NEDA) assessment. This longitudinal, retrospective, single-cohort analysis assessed MRI and clinical disease outcomes in patients with relapsing-remitting MS treated with natalizumab for up to 5 years in Prague, the Czech Republic. Methods The primary study endpoint was the proportion of patients free of new or enlarging fluid-attenuated inversion recovery (FLAIR) lesions after at least 2 years of natalizumab treatment. Secondary endpoints included percentage brain volume change over time, the number of new T1-hypointense lesions that persisted for ≥6 months, FLAIR and T1-hypointense lesion volume change over time, and the proportion of patients with NEDA-3 (defined as no relapses, no confirmed disability worsening, and no new or enlarging FLAIR lesions). Results A total of 193 patients were included in the study. During year 1 of natalizumab treatment, 78.9% of patients had no new or enlarging FLAIR lesions and 79.5% had no new T1 lesions. These proportions increased in years 2–5, with ≥98.0% of patients free of new or enlarging FLAIR lesions and ≥98.8% free of new T1 lesions. During year 1 on natalizumab, 52.2% of patients achieved NEDA-3; this proportion increased to ≥69.2% in years 2–5. Conclusion This study provides additional evidence that long-term MS disease activity, as measured by both MRI activity and NEDA-3, is well-controlled in patients treated with natalizumab in real-world settings.

The reduction and rationalisation of antibiotics used in mastitis control is an increasingly important area of dairy herd health to consider with clients due to the outcomes of the O'Neill Report and the requirement to reduce overall usage of antibiotic in the livestock sector at a national level. There is much interest around a range of strategies to achieve a reduction in antibiotic use on farm, for example cow-side treatment decisions to not use intramammary therapy and deciding not to treat low cell count cows at drying-off. However, the key driver of a reduction in antibiotic use on farm remains strategies to reduce the rate of new clinical cases and new cell count infections on farm, and therefore avoiding the need for antibiotic treatment of infected cows. This article reviews some recent research literature around some of the areas mentioned, and seeks thoughts from panel members active in mastitis control on farm.

RADA16 is a widely studied polypeptide known for its ability to self-assemble into β -sheets that form nanofibers. Here we show that it is possible to self-crosslink the molecule via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) as aqueous solutions. The product results in a mix of nanocrystals and near micron-size spherules. SEM and TEM pictures provide a view of the structures and nano tracking analysis gives their size distributions. FTIR analysis provides evidence for the existence of a crosslinking reaction.

The use of antimicrobials in food producing animals receives much attention and is highlighted as a focus in the efforts against antimicrobial resistance documented in the O'Neil report (2015) , with DEFRA committing to a