Development and evaluation of predictive models for pregnancy risk in UK dairy cows.

One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated and each herd was randomly allocated, at a ratio of 80:20, to either training or testing data sets. Following data cleaning, there were a total of 78 herds in the training data set and 20 herds in the testing data set. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59 and 0.62 in the nulliparous, primiparous and multiparous subsets, respectively. The models were generally well-calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012) and 0.020 (0.008) in the nulliparous, primiparous and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision-making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

Protocol for VIVALDI social care: Pilot study to reduce infections, outbreaks and antimicrobial resistance in care homes for older adults.

Care home residents are vulnerable to severe outcomes from infections such as COVID-19 and influenza. However, measures to control outbreaks, such as care home closures to visitors and new admissions, have a detrimental impact on their quality of life. Many infections and outbreaks could be prevented but the first step is to measure them reliably. This is challenging in care homes due to the lack of data and research infrastructure. During the pandemic, the VIVALDI study measured COVID-19 infections in residents and staff by partnering with care providers and using routinely collected data. This study aims to establish sentinel surveillance and a research database to enable observational and future interventional studies in care homes. The project has been co-produced with care providers, staff, residents, relatives, and researchers. The study (October 2023 to March 2025) will explore the feasibility of establishing a network of 500-1500 care homes for older adults in England that is underpinned by a linked data platform. No data will be collected from staff. The cohort will be created by regularly extracting resident identifiers from Digital Social Care Records (DSCR), followed by pseudonymisation and linkage to routinely collected datasets. Following extensive consultation, we decided not to seek informed consent from residents for data collection, but they can 'opt out' of the study. Our goal is to be inclusive, and it is challenging to give every resident the opportunity to 'opt in' due to cognitive impairment and the requirement for consultees. The project, and all requests to use the data will be overseen by relatives, residents, staff, and care providers. The study has been approved by the Health Research Authority Confidentiality Advisory Group (23/CAG/0134&0135) and the South-West Frenchay Research Ethics Committee (23/SW/0105). It is funded by the UK Health Security Agency.

Infections like flu or COVID-19 are common in care homes and infected residents can become seriously unwell. When infections spread, the measures that are often used to stop outbreaks, like care home closures to visitors and new admissions, can have a detrimental impact on residents. The first step to solving this problem is being able to measure how often infections and outbreaks happen, and how this varies across care homes. This is currently difficult because there are no systems to collect data from care home residents. During the COVID-19 pandemic, care homes worked with researchers and Government to deliver a research study called VIVALDI which measured COVID-19 infections in residents and staff and monitored what happened to them. This pilot study builds on what we learned in the pandemic and aims to reduce the impact of common infections on residents. We will set up a network of 500-1500 care homes for older adults in England that are interested in research. By collecting limited data (NHS numbers) from residents in these homes and linking to other datasets already held in the secure NHS environment, we can measure the extent of infections in residents. We are not collecting data from staff, and any residents in the datasets cannot be identified. We will also create an anonymous database (names, dates of birth, NHS numbers removed), which researchers can use to find new ways to prevent infection in care homes. This will be stored securely by the research team. If the project is successful, and residents and relatives support it, we hope this approach can be used permanently to monitor infections in care homes. The study has been designed in partnership with care providers, experienced care staff, policymakers, academics, and residents and their relatives, who will also oversee the study and all research outputs.

Highly Efficient Wide Bandgap Perovskite Solar Cells With Tunneling Junction by Self-Assembled 2D Dielectric Layer.

Reducing non-radiative recombination and addressing band alignment mismatches at interfaces remain major challenges in achieving high-performance wide-bandgap perovskite solar cells. This study proposes the self-organization of a thin two-dimensional (2D) perovskite BA2PbBr4 layer beneath a wide-bandgap three-dimensional (3D) perovskite Cs0.17FA0.83Pb(I0.6Br0.4)3, forming a 2D/3D bilayer structure on a tin oxide (SnO2) layer. This process is driven by interactions between the oxygen vacancies on the SnO2 surface and hydrogen atoms of the n-butylammonium cation, aiding the self-assembly of the BA2PbBr4 2D layer. The 2D perovskite acts as a tunneling layer between SnO2 and the 3D perovskite, neutralizing the energy level mismatch and reducing non-radiative recombination. This results in high power conversion efficiencies of 21.54% and 19.16% for wide-bandgap perovskite solar cells with bandgaps of 1.7 and 1.8 eV, with open-circuit voltages over 1.3 V under 1-Sun illumination. Furthermore, an impressive efficiency of over 43% is achieved under indoor conditions, specifically under 200 lux white light-emitting diode light, yielding an output voltage exceeding 1 V. The device also demonstrates enhanced stability, lasting up to 1,200 hours.

Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows.

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

Footbathing and Foot Trimming, and No Quarantine: Risks for High Prevalence of Lameness in a Random Sample of 269 Sheep Flocks in England, 2022.

Since 2004, the prevalence of lameness in sheep flocks in England has reduced as farmers have adopted evidence-based management practices to control lameness. In 2011, the Farm Animal Welfare Council proposed a target prevalence of <2% lameness in sheep by 2021. This study investigated whether that target had been achieved and determined which practices were associated with prevalence of lameness. A postal questionnaire was sent to 1000 randomly selected farmers to investigate the prevalence of lameness and management practices in 2022. The geometric mean prevalence of lameness was <2% in ewes and lambs, but the median was 3%; approximately 26% flocks had <2% lameness. Data were analysed using robust variable selection with multivariable linear models. Farmers that quarantined ewes for ≥3 weeks and did not use foot bathing or foot trimming to prevent lameness had 40-50% lower prevalence of lameness than those not using these practices. Fewer farmers (19.0%) were always using parenteral antimicrobials to treat footrot, an effective practice, than in previous research (49.7%). We conclude that the target of <2% lameness in England has been achieved by 26% of farmers, and further work is required for more farmers to follow the evidence-based management practices to minimise lameness.

Using Object-Oriented Simulation to Assess the Impact of the Frequency and Accuracy of Mobility Scoring on the Estimation of Epidemiological Parameters for Lameness in Dairy Herds.

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60-100%), and specificities (95-100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are <100%. This effect increases with more frequent scoring. Lameness prevalence was the only parameter that could be estimated with reasonable accuracy when simulating quarterly mobility scoring. These findings can help inform mobility scoring practices and the interpretation of mobility scoring data.

Interpreting Wide-Complex Tachycardia With the Use of Artificial Intelligence.

BACKGROUND: Adopting artificial intelligence (AI) in medicine may improve speed and accuracy in patient diagnosis. We sought to develop an AI algorithm to interpret wide-complex tachycardia (WCT) electrocardiograms (ECGs) and compare its diagnostic accuracy with that of cardiologists. METHODS: Using 3330 WCT ECGs (2906 supraventricular tachycardia [SVT] and 424 ventricular tachycardia [VT]), we created a training/validation (3131) and a test set (199 ECGs). A convolutional neural network structure using a modification of differentiable architecture search was developed to differentiate between SVT and VT. RESULTS: The mean accuracy of electrophysiology (EP) cardiologists was 92.5% with sensitivity 91.7%, specificity 93.4%, positive predictive value 93.7%, and negative predictive value 91.7%. Non-EP cardiologists had an accuracy of 73.2 ± 14.4% with sensitivity, specificity, and positive and negative predictive values of 59.8 ± 18.2%, 93.8 ± 3.7%, 93.6 ± 2.3%, and 73.2 ± 14.4%, respectively. AI had superior sensitivity and accuracy (91.9% and 93.0%, respectively) than non-EP cardiologists and similar performance compared with EP cardiologists. Mean time to interpret each ECG varied from 10.1 to 13.8 seconds for EP cardiologists and from 3.1 to 16.6 seconds for non-EP cardiologists. AI required a mean of 0.0092 ± 0.0035 seconds for each ECG interpretation. CONCLUSIONS: AI appears to diagnose WCT with accuracy superior to non-EP cardiologists and similar to EP cardiologists. Using AI to assist with ECG interpretations may improve patient care.

Energy Resolved Mass Spectrometry for Interoperable Non-resonant Collisional Spectra in Metabolomics.

In untargeted metabolomics, the unambiguous identification of metabolites remains a major challenge. This requires high-quality spectral libraries for reliable metabolite identification, which is essential for translating metabolomics data into meaningful biological information. Several attempts have been made to generate reproducible product ion spectra (PIS) under a low collision energy (ELab) regime and nonresonant collisional conditions but have not fully succeeded. We examined the ERMS (energy-resolved mass spectrometry) breakdown curves of two lipo-amino acids and showed the possibility to highlight "singular points", called descriptors hereafter (linked to respective ELab depending on the instrument), for each of the monomodal product ion profiles. Using several instruments based on different technologies, the PIS recorded at these specific ELab sites shows remarkable similarities. The descriptors appeared as being independent of the fragmentation mechanisms and can be used to overcome the main instrumental effects that limit the interoperability of spectral libraries. This proof-of-concept study, performed on two particular lipo-amino acids, demonstrates the high potential of ERMS-derived information to determine the instrument-specific ELab at which PIS recorded in nonresonant conditions become highly similar and instrument-independent, thus comparable across platforms. This innovative but straightforward approach could help remove some of the obstacles to metabolite identification in nontargeted metabolomics, putting an end to a challenging chimera.

Adherent skin barrier drape use is associated with a reduced risk of cardiac implantable device infection: Results from a prospective study of 14,225 procedures.

Background: Cardiac implantable electronic device (CIED) infection is a costly and highly morbid complication. Perioperative interventions, including the use of antibiotic pouches and intensified perioperative antibiotic regimens, have demonstrated marginal efficacy at reducing CIED infection. Additional research is needed to identify additional interventions to reduce infection risk. Objective: We sought to evaluate whether adherent skin barrier drape use is associated with a reduction in CIED infection. Methods: A prospective registry of all CIED implantation procedures was established at our institution in January 2007. The registry was established in collaboration with our hospital infection prevention team with a specific focus on prospectively identifying all potential CIED infections. All potential CIED infections were independently adjudicated by 2 physicians blinded to the use of an adherent skin barrier drape. Results: Over a 13-year period, 14,225 procedures were completed (mean age 72 ± 14 years; female 4,918 (35%); new implants 10,005 (70%); pulse generator changes 2585 (18%); upgrades 1635 (11%). Of those, 2469 procedures (17.4%) were performed using an adherent skin barrier drape. There were 103 adjudicated device infections (0.73%). The infection rate in patients in the barrier use groups was 8 of 2469 (0.32%) as compared with 95 of 11,756 (0.8%) in the nonuse group (P = .0084). In multivariable analysis, the use of an adherent skin barrier drape was independently associated with a reduction in infection (odds ratio 0.32; 95% confidence interval 0.154-0.665; P = .002). Conclusion: The use of an adherent skin barrier drape at the time of cardiac device surgery is associated with a lower risk of subsequent infection.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Unveiling the Role of Ge in CZTSSe Solar Cells by Advanced Micro-To-Atom Scale Characterizations.

Kesterite is an earth-abundant energy material with high predicted power conversion efficiency, making it a sustainable and promising option for photovoltaics. However, a large open circuit voltage Voc deficit due to non-radiative recombination at intrinsic defects remains a major hurdle, limiting device performance. Incorporating Ge into the kesterite structure emerges as an effective approach for enhancing performance by manipulating defects and morphology. Herein, how different amounts of Ge affect the kesterite growth pathways through the combination of advanced microscopy characterization techniques are systematically investigated. The results demonstrate the significance of incorporating Ge during the selenization process of the CZTSSe thin film. At high temperature, the Ge incorporation effectively delays the selenization process due to the formation of a ZnSe layer on top of the metal alloys through decomposition of the Cu-Zn alloy and formation of Cu-Sn alloy, subsequently forming of Cu-Sn-Se phase. Such an effect is compounded by more Ge incorporation that further postpones kesterite formation. Furthermore, introducing Ge mitigates detrimental "horizontal" grain boundaries by increasing the grain size on upper layer. The Ge incorporation strategy discussed in this study holds great promise for improving device performance and grain quality in CZTSSe and other polycrystalline chalcogenide solar cells.

Quantification of the effect of in utero events on lifetime resilience in dairy cows.

Currently, the dairy industry is facing many challenges that could affect its sustainability, including climate change and public perception of the industry. As a result, interest is increasing in the concept of identifying resilient animals, those with a long productive lifespan, as well as good reproductive performance and milk yield. There is much evidence that events in utero, that is, the developmental origins of health and disease hypothesis, alter the life-course health of offspring and we hypothesized that these could alter resilience in calves, where resilience is identified using lifetime data. The aim of this study was to quantify lifetime resilience scores (LRS) using an existing scoring system, based on longevity with secondary corrections for age at first calving and calving interval, and to quantify the effects of in utero events on the LRS using 2 datasets. The first was a large dataset of cattle on 83 farms in Great Britain born from 2006 to 2015 and the second was a smaller, more granular dataset of cattle born between 2003 and 2015 in the Langhill research herd at Scotland's Rural College. Events during dam's pregnancy included health events (lameness, mastitis, use of an antibiotic or anti-inflammatory medication), the effect of heat stress as measured by temperature-humidity index, and perturbations in milk yield and quality (somatic cell count, percentage fat, percentage protein and fat:protein ratio). Daughters born to dams that experienced higher temperature-humidity indexes while they were in utero during the first and third trimesters of pregnancy had lower LRS. Daughter LRS were also lower where milk yields or median fat percentages in the first trimester were low, and when milk yields were high in the third trimester. Dam LRS was positively associated with LRS of their offspring; however, as parity of the dam increased, LRS of their calves decreased. Similarly, in the Langhill herd, dams of a higher parity produced calves with lower LRS. Additionally, dams that recorded a high maximum locomotion score in the third trimester of pregnancy were negatively associated with lower calf LRS in the Langhill herd. Our results suggest that events that occur during pregnancy have lifelong consequences for the calf's lifetime performance. However, experience of higher temperature-humidity indexes, higher dam LRS, and mothers in higher parities explained a relatively small proportion of variation in offspring LRS, which suggests that other factors play a substantial role in determining calf LRS. Although "big data" can contain a considerable amount of noise, similar findings between the 2 datasets indicate it is likely these findings are real.

RAFF-5 Study to Improve the Quality and Safety of Care for Patients Seen in the Emergency Department With Acute Atrial Fibrillation and Flutter.

BACKGROUND: We sought to improve the immediate and subsequent care of emergency department (ED) patients with acute atrial fibrillation (AF) and flutter (AFL) by implementing the principles of the Canadian Association of Emergency Physicians AF/AFL Best Practices Checklist. METHODS: This cohort study included 3 periods: before (7 months), intervention introduction (1 month), and after (7 months), and was conducted at a major academic centre. We included patients who presented with an episode of acute AF or AFL and used multiple strategies to support ED adoption of the Canadian Association of Emergency Physicians checklist. We developed new cardiology rapid-access follow-up processes. The main outcomes were unsafe and suboptimal treatments in the ED. RESULTS: We included 1108 patient visits, with 559 in the before and 549 in the after period. In a comparison of the periods, there was an increase in use of chemical cardioversion (20.6% vs 25.0%; absolute difference [AD], 4.4%) and in electrical cardioversion (39.2% vs 51.2%; AD, 12.0%). More patients were discharged with sinus rhythm restored (66.9% vs 75.0%; AD, 8.1%). The proportion seen in a follow-up cardiology clinic increased from 24.2% to 39.9% (AD, 15.7%) and the mean time until seen decreased substantially (103.3 vs 49.0 days; AD, -54.3 days). There were very few unsafe cases (0.4% vs 0.7%) and, although there was an increase in suboptimal care (19.5% vs 23.1%), overall patient outcomes were excellent. CONCLUSIONS: We successfully improved the care for ED patients with acute AF/AFL and achieved more frequent and more rapid cardiology follow-up. Although cases of unsafe management were uncommon and patient outcomes were excellent, there are opportunities for physicians to improve their care of acute AF/AFL patients. GOV IDENTIFIER: NCT05468281.

Multifunctional Surface Treatment against Imperfections and Halide Segregation in Wide-Band Gap Perovskite Solar Cells.

Mixed-halide wide-band gap perovskites (WBPs) still suffer from losses due to imperfections within the absorber and the segregation of halide ions under external stimuli. Herein, we design a multifunctional passivator (MFP) by mixing bromide salt, formamidinium bromide (FABr) with a p-type self-assembled monolayer (SAM) to target the nonradiative recombination pathways. Photoluminescence measurement shows considerable suppression of nonradiative recombination rates after treatment with FABr. However, WBPs still remained susceptible to halide segregation for which the addition of 25% p-type SAM was effective to decelerate segregation. It is observed that FABr can act as a passivating agent of the donor impurities, shifting the Fermi-level (Ef) toward the mid-band gap, while p-type SAM could cause an overweight of Ef toward the valence band. Favorable band bending at the interface could prevent the funneling of carriers toward I-rich clusters. Instead, charge carriers funnel toward an integrated SAM, preventing the accumulation of polaron-induced strain on the lattice. Consequently, n-i-p structured devices with an optimal MFP treatment show an average open-circuit voltage (VOC) increase of about 20 mV and fill factor (FF) increase by 4% compared with the control samples. The unencapsulated devices retained 95% of their initial performance when stored at room temperature under 40% relative humidity for 2800 h.

Cd-Free Pure Sulfide Kesterite Cu2 ZnSnS4 Solar Cell with Over 800 mV Open-Circuit Voltage Enabled by Phase Evolution Intervention.

The Cd-free Cu2 ZnSnS4 (CZTS) solar cell is an ideal candidate for producing low-cost clean energy through green materials owing to its inherent environmental friendliness and earth abundance. Nevertheless, sulfide CZTS has long suffered from severe open-circuit voltage (VOC ) deficits, limiting the full exploitation of performance potential and further progress. Here, an effective strategy is proposed to alleviate the nonradiative VOC loss by manipulating the phase evolution during the critical kesterite phase formation stage. With a Ge cap layer on the precursor, premature CZTS grain formation is suppressed at low temperatures, leading to fewer nucleation centers at the initial crystallization stage. Consequently, the CZTS grain formation and crystallization are deferred to high temperatures, resulting in enhanced grain interior quality and less unfavorable grain boundaries in the final film. As a result, a champion efficiency of 10.7% for Cd-free CZTS solar cells with remarkably high VOC beyond 800 mV (63.2% Schockley-Queisser limit) is realized, indicating that nonradiative recombination is effectively inhibited. This strategy may advance other compound semiconductors seeking high-quality crystallization.

Improvement of mammary gland health in 81 'sentinel herds' in England and Scotland between 2012 and 2021.

BACKGROUND: Achieving a reduction in mastitis in dairy cows is a common industry goal, but there is no recent peer-reviewed record of progress in the UK. METHODS: A convenience sample of 125 herds in England and Scotland was recruited based on the quality of records in 2016, willingness to participate and representative geographical distribution. Individual cow somatic cell counts and clinical mastitis data from 2012 to 2021 were summarised annually, and temporal changes were analysed. Eighty-one herds had sufficient data for comparison between 2012 and 2021, for one or more parameters. RESULTS: Over this period, the median incidence rate of clinical mastitis decreased from 40.0 to 21.0 cases per 100 cows per year (p < 0.001), with improvement in both lactation and dry period indicators. Lactation new infection rate calculated from individual cow somatic cell counts fell from 8.75% to 5.95% (p < 0.001), dry period new infection rate fell from 16.8% to 14.1% (p < 0.05) and proportion of cows over 200,000 cells/mL fell from 20.0% to 14.3% (p < 0.001). LIMITATIONS: Data were necessarily from herds with good records and do not provide absolute values for the industry. CONCLUSION: The findings reflect good progress over a 10-year period in a cohort of well-recorded herds and align with other national datasets.

Do barrier dressings reduce cardiac implantable device infection: Protocol for a randomized controlled trial (BARRIER-PROTECT).

Background: Cardiac implantable electronic device (CIED) procedures can be associated with serious complications, including infection with significant mortality and morbidity, necessitating removal of the device and prolonged hospitalization. One potential pathophysiological mechanism is pocket contamination at the time of device implantation. Therefore, steps taken to prevent contamination at this stage can potentially reduce CIED infections.The barrier dressing, an adhesive material applied to the skin, has the potential to reduce the colonization of the surgical site with host flora that can predispose to infection. There are a limited number of randomized prospective studies on barrier dressing use during various surgeries, but it has never been systematically studied in CIED implantation. Objectives: Do Barrier Dressings Reduce Cardiac Implantable Device Infection? (BARRIER-PROTECT trial; NCT04591366) is a single-centre, prospective, double-armed, single-blinded, randomized controlled trial designed to evaluate the use of an intra-operative adhesive barrier dressing to reduce the risk of end-of-procedure pocket swab positivity. We hypothesize that adhesive draping during implant procedures will reduce the risk of contamination from the skin flora. Also, we aim to investigate if the end-of-procedure pocket swab culture positivity can be used as a potential surrogate marker of CIED infection. Methods and Design: Patients undergoing a second or later procedure on the same device pocket (pulse generator change, lead/pocket revision or upgrade) will be enrolled. Eligible and consenting patients will be equally randomized to the use of barrier dressing or not using an automated web-based system. Patients, but not the operator, will be blinded to the arm. The person performing the pocket swabs will also be blinded. The primary endpoint is the end-of-procedure pocket swab culture positivity. The main secondary endpoint is the CIED infection rate. Discussion: This is the first randomized controlled trial to assess the effectiveness of using a barrier adhesive draping on reducing the end-of-procedure pocket swab culture positivity. In this study, we are exploring a low-cost intervention that may significantly reduce CIED infection. Also, having a valid surrogate marker for CIED infection at the time of implant will facilitate design of future clinical trials.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Barrier Strategy for Strain-Free Encapsulation of Perovskite Solar Cells.

The performance loss caused by encapsulation has been an obstacle to guarantee the excellent power conversion efficiency of perovskite solar cells (PSCs) in practical application. This work revealed that the encapsulation-induced performance loss is highly related to the tensile strains imposed on the functional layers of the device when the PSC is exposed directly to the deformed encapsulant. A barrier strategy is developed by employing a nonadhesive barrier layer to isolate the deformed encapsulant from the PSC functional layer, achieving a strain-free encapsulation of the PSCs. The encapsulated device with a barrier layer effectively reduced the relative performance loss from 21.4% to 5.7% and dramatically improved the stability of the device under double 85 environment conditions. This work provides an effective strategy to mitigate the negative impact of encapsulation on the performance of PSCs as well as insight into the underlying mechanism of the accelerated degradation of PSCs under external strains.