Similar Models Research Articles

HelpUsGreen LLP: driving next-level growth by building a sustainable circular economy

Learning outcomes On completion of this case study, students will be able to describe a circular economy and cross-marketing; discuss the common strategies under the circular economy paradigm; describe the benefits and risks of cross-marketing brand alliances and their strategic implications; examine the role of growth strategy in driving the growth of an organization; classify what constitutes the elements of a strategy for HelpUsGreen LLP; and discuss measures to be considered while selecting a partner for a cross-marketing brand alliance. Case overview/synopsis HelpUsGreen LLP was a start-up founded by Karan Rastogi in 2019, which manufactured incense sticks using the circular economy model. After deflecting from his earlier partner in 2019 and after approximately two years of effort, Rastogi expanded his business of making incense sticks using a circular economy model. Students through this case can step into the shoes of Rastogi, who, as of 9 January 2023, was in Kanpur and was in an urgent need to replicate a similar circular economy model in different cities. With over 21,060 tonnes of temple flower waste recycled, 210 tonnes of chemical pesticides offset and 100% natural products delivered, multiple opportunities emerged at the cross-marketing brand alliance and new product segment levels. Rastogi entered into several cross-marketing brand alliance arrangements and tasted success. Rastogi believed that choosing the right partner for cross-marketing was key to successful cooperation. Considering cross-marketing as a way forward, he urgently needed to develop a strategy that aligned with his mission to take HelpUsGreen LLP’s circular economy model to other parts of the country. Students should take into consideration the data regarding the processes at HelpUsGreen LLP that helped Rastogi restart from scratch and make assumptions to decide the growth route for entering different cities. Complexity academic level This case can be used as an introductory case in a post-graduate class on growth strategy for a business based on the circular economy model, as it delineates the challenges faced by a firm while creating a circular economy and managing its growth phase. The case can also be used in an entrepreneurship management course and a strategic management course. This case allows students to learn about the circular economy and challenges faced by the company during the growth phase. Thus, the case can be used for covering multiple perspectives related to growth strategy (e.g. the application of Ansoff matrix), for defining what is cross-marketing brand alliance and discussing what measures need to be considered while selecting a partner for cross-marketing brand alliance, and it is ideal for teaching the elements of strategy. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

A T5-based interpretable reading comprehension model with more accurate evidence training

Pre-trained language models (PLMs) have achieved outstanding performance on Machine Reading Comprehension (MRC) tasks, but these models’ interpretability remains uncertain. In this paper, we exploit the strengths of the pre-trained T5 (Text-to-Text Transfer Transformer) model on evidence inference to improve the interpretability of the MRC model and propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for the generated answers. First, we propose a novel T5-based Semantic Textual Similarity (STS) model to label training evidences more precisely, including label reconstruction and data augmentation. Then, we propose a T5-based interpretable reading comprehension model with more accurate evidence training, including a threshold-based method to filter out erroneous evidence during model training. Experiments show that our model significantly outperforms the baseline BERT (Pseudo-data Training) model by 8.7 and 8.0 points for the evidence F1-score of SQuAD1.1 respectively in the base and large levels. Our code is available at https://github.com/MN-Guan/T5-InterMRC.

ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model.

The enzyme-linked immunosorbent spot (ELISpot) assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is used widely in the context of cancer and infectious diseases to validate the immunogenicity of predicted epitopes. While technological advances have kept pace with the demand for increased throughput, efforts to increase scale are bottlenecked by current assay design and deconvolution methods, which have remained largely unchanged. Current methods for designing pooled ELISpot experiments offer limited flexibility of assay parameters, lack support for high-throughput scenarios and do not consider peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles the deconvolution of positive peptides using assay readouts. In this study, we present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 model that groups immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays compared to existing combinatorial approaches. To validate ACE's performance on real-world datasets, we conducted a comprehensive benchmark study, contextualizing design choices with their impact on prediction quality. Our results demonstrate ACE's capacity to further increase precision of identified immunogenic peptides, directly optimizing experimental efficiency. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR).

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.

A new dataset of oral panoramic x-ray images and parallel network using transformers for medical image segmentation

IntroductionAccurate segmentation of the key mandibular region in the oral panoramic X-ray image is crucial for the diagnosis of the mandibular region and the planning of implant surgery. Because the oral panoramic X-ray image contains many important anatomical information for implant treatment evaluation. However, the fuzzy boundary between each region in the image makes the segmentation task very challenging. In data-driven segmentation methods, corresponding datasets are often required. Due to the limited oral data set at present, there is a bottleneck in clinical application. Materials and methodsIn this paper, we build a panoramic X-ray image dataset for the mandibular region. The dataset has a total of 711 images. The dataset is divided into 8 categories based on the number of teeth and treatment conditions. The annotations include mandible, normal teeth, treated teeth and implants. In terms of network segmentation. According to the local and global characteristics of the dataset, we designed a CBTrans partition network by paralleling the convolution block and the Swin-transform block of the bottleneck structure. ResultsThe experimental results show that our proposed network achieves excellent performance on the mandibular region segmentation dataset and the common retina dataset DRIVE. ConclusionCBTrans can better extract features locally and globally by combining CNN of the bottleneck structure and Swin Transformer in parallel. CBTrans demonstrates performance advantages over other similar hybrid architecture models.

A multi-taxonomic survey to determine the conservation status of native pollinators

The worldwide decline of insects calls for understanding the conservation status of key insect groups at local and regional scales. Pollinators are widely recognized as especially important to native ecosystems and agriculture and their declines have been met with calls for inventory and monitoring. In New York State, we conducted a four-year survey effort to document the distribution and status of key native pollinators in four insect orders: Coleoptera, Diptera, Hymenoptera, and Lepidoptera. In this paper, we describe the process of designing and implementing the study in the hopes that other jurisdictions could follow a similar model. We combined systematic field inventories, surveys of special habitats, target species surveys, community science observations, and museum and partner data to create a comprehensive look at the distribution of each of 457 species in two time periods (2000 to present and 1999 and earlier). We used the conservation status ranking system developed by NatureServe and generated ranks and a series of distribution maps and phenology charts for each species. We describe the effort needed to carry out the study, as well as reasons for its success and areas for improvement.

A cost-analysis of managing secondary and apparent treatment-resistant hypertension in a specialist multidisciplinary hypertension clinic.

A knowledge gap exists around the costs and budget impact of specialist hypertension clinics. This study reports on the cost of providing care in a multidisciplinary hypertension clinic staffed by nephrologist, endocrinologist and cardiologist, which manages patients with suspected secondary hypertension and/or apparent treatment-resistant hypertension. The aim of this study is to provide the evidence required to inform policy and planning care pathways for this patient group. A cost analysis from a healthcare provider perspective using micro-costing techniques was conducted to estimate the direct implementation costs of existing standard practice for the care pathway of patients attending the multidisciplinary hypertension clinic. Sixty-five patients originally recruited for a study of medication adherence in hypertension were included in the sample. The total care-pathway cost per patient, taking into account clinic visits, clinical reviews, investigations and MDT discussion, was estimated to be €3277, on average. For the patient subgroups, the average cost was €5644 for patients diagnosed with primary aldosteronism and €1446 for patients diagnosed with essential hypertension. There is significant cost associated with providing specialized hypertension care for patients with apparent treatment-resistant hypertension. Given the high rates of nonadherence in this population, it is likely that some of this cost could be avoided with better detection and management of medication adherence in this challenging population. Future studies should consider the cost-effectiveness of this or similar models of care by exploring the benefit to patients and the wider healthcare context of providing care of this type.

Opening the Higgs portal to lepton-flavoured dark matter

We study a simplified model of lepton-flavoured complex scalar dark matter coupling to right-handed leptons and the Higgs boson. The model is set up in the Dark Minimal Flavour Violation framework. In contrast to previous studies of similar models we consider the most general case and do not a priori constrain the hierarchy of dark matter masses and couplings in any way aside from the chosen parameter ranges. In the first part of the analysis we discuss the impact of Higgs portal interactions and the generalised mass hierarchy on the model’s phenomenology. We find that they render new physics masses around the electroweak scale viable, thus qualifying this model to address the (g − 2)μ puzzle. After reviewing the current situation of the latter, we perform two combined analyses — one in which (g − 2)μ allows for significant new physics effects and one in which it does not. We find that while the latter scenario allows for a larger range of new physics scales, both scenarios are equally viable.

Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics.

Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).

Pathways to community living in practice: Local development and adaptation within an older peoples mental health service

Objective To describe the local adaptation of the Pathways to Community Living (PCLI) program in an Older Peoples Mental Health (OPMH) service to guide other services. Method A retrospective observational study was conducted. Data were obtained from service planning meetings and newly developed documents, Clinical Advisory Committee meetings, and OPMH PCLI database. Results The PCLI program was adapted for the local OPMH service through development of an assessment template, creating a Memorandum of Understanding with a partner Residential Aged Care Facility (RACF) and establishing processes for collaboration and regular review. Between 2019 and March 2023, 20 mental health consumers were referred to the OPMH PCLI program. Their demographic and clinical characteristics are described. Conclusions Adaptation of the PCLI program for OPMH consumers required consideration of specific older adult needs to develop a bespoke plan for assessment and partnership with the PCLI-funded RACF. The development phase and ongoing processes for review facilitated engagement of key stakeholders across health and RACF sectors, highlighting issues with consumer engagement. Similar models could be used by other health services to implement the PCLI in their local context.

Mapping property redevelopment via GeoAI: Integrating computer vision and socioenvironmental patterns and processes

Domain knowledge of social and environmental sciences is generally derived from less structured small data and/or small models. The integration of deep learning with socioenvironmental and geographical patterns and processes is still in an early phase. This study proposes a flexible framework that synthesizes computer vision with patterns and processes of geographical phenomena (e.g., urban redevelopment) via deep convolutional neural networks and spatiotemporal neighborhoods, respectively. Meanwhile, undesirable visual cues (e.g., temporary objects such as cars) cause false alarms in urban change detection. Thus, a masked deep pyramid similarity model (i.e., the computer vision model) is proposed to minimize the negative impact of nonbuilding changes. This pipeline has robustness against obfuscation of undesirable street scene changes and elasticity of geographical knowledge representations in mapping property redevelopment. This model is reproducible and adaptable due to the wide availability of SVI and flexibility of the framework. The results suggest that the domain-driven rules and nonbuilding change masking mechanism can significantly increase the accuracy of a computer vision model. We also find that urban redevelopment should be understood as a locally tuned back-to-the-city process with weak replicability. Hybrid gentrification (i.e., the combination of “seesaw” gentrification and continuous gentrification) is observed globally with local variations. Takeaway for practiceFirst, the population of those affected should be carefully identified and the benefits of gentrification need to be redistributed on a larger scale. For Auckland, decision-makers should not divert attention away from low-income tenants in outer suburbs that slipped below the radar of local government and scholars. Second, more attention should be given to less resourcing neighborhoods in constantly gentrifying neighborhoods when governments formulate and implement housing subsidy policies. Locally, incessant redevelopment in Auckland CBD, New Market and Remuera, etc., should be contained.

Accounting Practices and Guiding Principles Influencing Shared Value Creation

This study explored the shared value and the types. Porter and Kramer’s shared-value model is analysed; its critiques are outlined, and other approaches along with their weaknesses suggested by previous researchers, are explored. A documentary research approach, which consists of reviewing, analysing and examining information was adopted. The sources included journal articles, books, frameworks and guidelines. The results of the study indicated/revealed that value is not only accounted for in financial terms but also in intangible drivers, value creation processes result in predetermined output and outcomes which cannot be predetermined. The study also indicated that shared- value is a term that is not distinctively and commonly used as its components were not clearly spelt out. Researchers/ scholars have failed to distinguish it from other similar models of social and environmental responsibility. The study concludes that creating shared value from social and relations capital contributes towards increased productivity and sustainability of a company.

Perspectives of women and partners from migrant and refugee backgrounds accessing the Cross Cultural Worker Service in maternity and early childhood services—a survey study

BackgroundWomen from migrant and refugee backgrounds living in high-income countries have increased risk of adverse perinatal outcomes and report lower satisfaction with perinatal healthcare. In Sydney, Australia, a new service known as the Cross Cultural Workers (CCWs) in Maternity and Child and Family Health Service (the CCW Service) was implemented to support such women and families from pregnancy to the early parenting period. This study aimed to ascertain the experiences of women and their partners engaging with the CCW Service.MethodsA survey study was undertaken. Women accessing the CCW Service were recruited during pregnancy and were asked to complete surveys at three time points: in the third trimester of pregnancy, at 6 and 12 months postpartum. Their partners were invited to complete a survey at 6 months postpartum. Survey data were analysed to compare satisfaction, usefulness, number of CCW interactions, cultural sensitivity, and service improvement recommendations across all three survey timepoints.ResultsA total of 231 surveys were received: 113 during pregnancy, 50 at 6-months postpartum, 44 at 12-months postpartum, and 24 partner surveys. Participants in all surveys reported the CCW Service to be useful (84–94%), stating that it increased their understanding of pregnancy, birth and parenting (95–100%), and that they would recommend the CCW Service (92–98%). Participants experienced a high level of satisfaction (88–95%) irrespective of the number of CCW interactions (p = 0.42). Thoughts on becoming a mother or parent were more positive after meeting the CCW than before for both women (p = 0.01) and partners (p = 0.12). Suggestions for CCW Service improvement were to 1) increase the provision of information, specifically financial entitlements, postnatal depression, and support services, 2) increase involvement of partners in care, 3) increase the CCW workforce/or number of CCWs.ConclusionThe CCW Service was associated with positive experiences and high rates of satisfaction at all timepoints. This service has the potential to inform the implementation of similar models of care that improve accessibility, the perinatal experience, and respond to the unique needs of women and families from migrant and refugee backgrounds.

Study on Proportioning Scheme of Coal System Rocky Similar Material Based on Orthogonal Test.

Similar materials play an important role in model testing. In order to meet the demand for similar materials in modeling tests, such as those on coal mining, coal system rocky similar materials were formulated using yellow sand as a coarse aggregate, heavy calcium carbonate as a fine aggregate, and cement and gypsum as binders. Based on the orthogonal experimental design method, four influencing factors, namely the aggregate-binder ratio, heavy calcium carbonate content, cement-gypsum ratio, and moisture content, were selected. Each factor was designed at five levels. Through weighing, uniaxial compression, Brazilian splitting, and variable-angle plate shear tests on 225 specimens under 25 different ratios, five physico-mechanical property indicators of the material, including density, compressive strength, tensile strength, cohesion, and internal friction angle, were obtained under different ratios. The test results indicate that the similar materials formulated with the above raw materials had a wide range of mechanical properties, which met the simulation needs of different types of coal rocks, such as main coking coal, anthracite, shale, etc., in the similar model test. Range analysis was adopted to analyze the sensitivities to each factor, which showed that the density and internal friction angle of similar materials are mainly controlled by the aggregate-binder ratio; the cement-gypsum ratio mainly controls the compressive strength, tensile strength, and cohesion of the material. Analysis of variance (ANOVA) was adopted to analyze the sensitivities to each factor, which showed that the aggregate-binder ratio had a highly significant effect on the density of the material, the cement-gypsum ratio had a highly significant effect on the compressive and tensile strength of the material, the cement-gypsum ratio had a significant effect on the cohesion and density of the material, and the moisture content had a significant effect on the compressive strength of the material. The remaining factors did not significantly affect the material parameters. The results of this study can provide some reference for the selection of coal system rocky similar materials in subsequent physical modeling tests.

Research on Similarity Simulation Experiment of Mine Pressure Appearance in Surface Gully Working Face Based on BOTDA.

In order to study the mountain deflection characteristics and the pressure law of the working face after the mining of a shallow coal seam under the valley terrain, a geometric size of 5.0 × 0.2 × 1.33 m is used in the physical similarity model. Brillouin optical time domain analysis (BOTDA) technology is applied to a similar physical model experiment to monitor the internal strain of the overlying rock. In this paper, the strain law of the horizontal optical fiber at different stages of the instability of the mountain structure is analyzed. Combined with the measurement of the strain field on the surface of the model via digital image correlation (DIC) technology, the optical fiber strain characteristics of the precursor of mountain instability are given. The optical fiber characterization method of working face pressure is proposed, and the working face pressures at different mining stages in gully terrain are characterized. Finally, the relationship between the deflection instability of the mountain and the strong ground pressure on the working face is discussed. The sudden increase in the strain peak point of the horizontally distributed optical fiber strain curve can be used to distinguish the strong ground pressure. At the same time, this conclusion is verified by comparing the measured underground ground pressure values. The research results can promote the application of optical fiber sensing technology in the field of mine engineering.

Changes in medical therapy after cardiac resynchronization in patients with heart failure: insights from the U.S. Veterans Affairs healthcare system

Abstract Background Patients with heart failure with reduced ejection fraction (HFrEF) are often not treated with comprehensive guideline directed medical therapy (GDMT) due to hemodynamic and renal limitations. Cardiac resynchronization therapy (CRT) improves hemodynamics in patients with HFrEF and ventricular dyssynchrony. Whether CRT facilitates intensification of GDMT in patients with HFrEF has not been well-studied. Purpose To assess pre-procedure GDMT (B-blocker, ACEI [angiotensin converting enzyme inhibitor]/ARB [angiotensin receptor blocker], ARNI [angiotensin receptor neprilysin inhibitor], MRA [mineralocorticoid receptor antagonist], SGLT2i [sodium-glucose cotransporter-2 inhibitor]) utilization in HFrEF patients who received CRT and to compare changes in GDMT in patients who received CRT with patients who received implantable cardioverter-defibrillator (ICD). Methods Veterans with left ventricular ejection fraction (LVEF) 35% who received CRT or ICD from Feb 22, 2000 – Jul 28, 2022 were identified. Patients with end-stage renal disease were excluded. Changes in GDMT 12-months following device implant were characterized and compared between CRT and ICD recipients. Mixed effects logistic regression models were generated to evaluate associations between receipt of CRT and the composite of therapy initiation, dose increase, or target dose achievement compared with ICD. Models were adjusted for clinical characteristics and pre-implant GDMT utilization. Similar models were developed to evaluate the adjusted associations between CRT and changes in estimated glomerular filtration rate (eGFR) and blood pressure (BP) compared with ICD. Results A total of 2,082 CRT recipients and 7,263 ICD recipients were identified. Pre-procedure heart rate, BP, serum potassium, and eGFR were within normal ranges and similar between groups. Prior to device implant, ACEI/ARB/ARNI were most frequently prescribed (91%), followed by -blockers (88%), MRA (46%), and SGLT2i (33% in post-SGLT2i era). Pre-procedure GDMT was similar between CRT and ICD recipients. At 12 months follow-up, CRT was independently associated with increased odds of therapy initiation, dose increase, or target dose achievement for ACEI/ARB/ARNI, ARNI, B-blockers, and MRA (Figure 1). ICD was associated with a decline in eGFR while CRT was associated with an increase in eGFR (mean [standard deviation]: -0.95 [0.17] vs. +0.6 [0.29]; p<0.01), while no differences were observed in BP or LVEF at 12 months. Conclusions In a large, contemporary cohort of patients with HFrEF, most patients were not prescribed MRA or SGLT2i prior to receiving CRT or ICD despite no apparent renal or blood pressure-related contraindications. CRT is associated with increased odds of the composite of initiation, dose increase, or target dose achievement for ACEI/ARB/ARNI, ARNI, B-blockers, and MRA compared with ICD. GDMT intensification may be mediated by beneficial changes in kidney function in CRT recipients.Figure 1

The added prognostic value of stress induced hyperglycemia to the GRACE risk score for the prediction of 1-year major adverse cardiovascular events in patients with ST-elevation myocardial infarction

Abstract Background There is growing evidence suggesting that Stress Induced Hyperglycemia (SIH) is a risk factor of poor prognosis in acute coronary syndrome (ACS)[1–3]. However, this parameter is not included in risk prediction scores, applied in clinical practice. Ιmportantly, there is no universally accepted definition for SIH especially in the setting of ACS. Across the existing evidence base, SIH is defined either based on admission blood glucose (ABG) levels or by calculating the glycemic gap and the stress hyperglycemia ratio (SHR)[4]. Purpose Considering the variability in SIH definitions and the need for further investigation on the long-term prognostic impact of SIH, we present herein original data concerning the prognostic role of SIH for long-term risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in patients presenting with ST-elevation myocardial infarction (STEMI) on top of a traditional prediction risk score such as the GRACE 2.0 risk score. Methods Our data were derived from a post-hoc analysis of a prospective study (NCT04580173). Every trial procedure was conducted according to the principles set by the declaration of Helsinki. Each participant provided written informed consent before being enrolled in the study. Our analysis included 309 consecutive STEMI patients who were diagnosed with SIH if ABG was > 140 mg/dl and a fasting period of at least 8 hours was confirmed. Each participant was followed-up for a median 1-year period. Baseline characteristics of the included patients are presented in Table 1. The records of all enrolled patients were retrospectively analyzed to calculate the GRACE 2.0 risk score for each patient. Results Cox regression analysis adjusted for age, gender, body mass index (BMI), hypertension, smoking, and SIH was performed but did not identify GRACE risk score>140 as an independent predictor of MACCE (aHR=1.408, 95% CI:0.764-2.594 p=0.273, Figure 1). On the other hand, a similar model adjusted for the same covariates identified the combination of GRACE risk score>140 and SIH as a significant predictor of MACCE occurrence (aHR=2.149, 95% CI:1.224-3.771, p=0.008). Conclusions Our results suggest that SIH combined with the GRACE 2.0 risk score was associated with improved prediction of the risk for 1-year MACCE in STEMI patients as compared to the GRACE 2.0 risk score alone. In this study, SIH was diagnosed based on the levels of admission blood glucose (after 8-hours of fasting) and not based on the SHR. Despite the heterogeneity in SIH definitions, stress hyperglycemia in the setting of STEMI might be an underrated yet robust prognostic marker not only for short term but also for long term adverse clinical events, especially when integrated in existing clinical risk scores.Survival curveBaseline characteristics

The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry.

COVID-19 led governments worldwide to enact a variety of containment and closure policies. Substantial attention has been directed toward the idea that these public health measures may have unanticipated negative side effects. One proposed effect relates to video games. There is a nascent evidence base suggesting that individuals played video games for longer and in a more disordered manner during lockdowns and school closures specifically. These increases are commonly framed as a potential health concern in relation to disordered gaming. However, the evidence base regarding changes in gaming during the COVID-19 pandemic is based on self-report and, thus, is susceptible to bias. Therefore, it is unclear what the true consequences of lockdowns were for gaming behavior worldwide. The primary objective of this study was to estimate whether any specific lockdown policy led to meaningful increases in the amount of time individuals spent playing video games. Rather than relying on self-report, we used >251 billion hours of raw gameplay telemetry data from 184 separate countries to assess the behavioral correlates of COVID-19-related policy decisions. A multilevel model estimated the impact of varying enforcement levels of 8 containment and closure policies on the amount of time that individual users spent in-game. Similar models estimated the impact of policy on overall playtime and the number of users within a country. No lockdown policy can explain substantial variance in playtime per gamer. School closures were uniquely associated with meaningful increases in total playtime within a country (r2=0.048). However, this was associated with increases in the number of unique individuals playing games (r2=0.057) rather than increases in playtime per gamer (r2<0.001). Previous work using self-report data has suggested that important increases in heavy gaming may occur during pandemics because of containment and closure ("lockdown") procedures. This study contrasts with the previous evidence base and finds no evidence of such a relationship. It suggests that significant further work is needed before increases in disordered or heavy gaming are considered when planning public health policies for pandemic preparedness.

Ruggedized Self-Propelling Hemostatic Gauze Delivers Low Dose of Thrombin and Systemic Tranexamic Acid and Achieves High Survival in Swine With Junctional Hemorrhage.

Hemorrhage is responsible for 91% of preventable prehospital deaths in combat. Bleeding from anatomic junctions such as the groin, neck, and axillae make up 19% of these deaths, and reports estimate that effective control of junctional hemorrhage could have prevented 5% of fatalities in Afghanistan. Hemostatic dressings are effective but are time-consuming to apply and are limited when proper packing and manual pressure are not feasible, such as during care under fire. CounterFlow-Gauze is a hemostatic dressing that is effective without compression and delivers thrombin and tranexamic acid into wounds. Here, an advanced prototype of CounterFlow-Gauze, containing a range of low thrombin doses, was tested in a lethal swine model of junctional hemorrhage. Outcomes were compared with those of Combat Gauze, the current dressing recommended by Tactical Combat Casualty Care. CounterFlow-Gauze containing thrombin doses of 0, 20, 200, and 500 IU was prepared. Swine received femoral arteriotomies, and CounterFlow-Gauze was packed into wounds without additional manual compression. In a separate study using a similar model of junctional hemorrhage without additional compression, CounterFlow-Gauze containing 500 IU thrombin was tested and compared with Combat Gauze. In both studies, the primary outcomes were survival to 3 h and volume of blood loss. CounterFlow-Gauze with 200 and 500 IU had the highest 3-h survival, achieving 70 and 75% survival, respectively. CounterFlow-Gauze resulted in mean peak plasma tranexamic acid concentrations of 9.6 ± 1.0 µg/mL (mean ± SEM) within 3 h. In a separate study with smaller injury, CounterFlow-Gauze with 500 IU achieved 100% survival to 3 h compared with 92% in Combat Gauze animals. An advanced preclinical prototype of CounterFlow-Gauze formulated with a minimized thrombin dose is highly effective at managing junctional hemorrhage without compression. These results demonstrate that CounterFlow-Gauze could be developed into a feasible alternative to Combat Gauze for hemorrhage control on the battlefield.