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Emergent climate protection strategies in German hospitals: A cluster analysis
Hospitals emit large amounts of greenhouse gas emissions during healthcare delivery due to their extensive resource utilization and substantial waste generation. By implementing climate actions, hospitals can significantly contribute to climate protection in healthcare. This paper delves into the climate protection efforts of German hospitals, with a specific focus on the emergent strategies of hospital administrative leaders and the employee engagement within the framework of Stakeholder Theory. The investigation is based on primary data from an online survey of hospital administrative leaders in German hospitals. Employing a hierarchical cluster analysis, the study identifies four distinct clusters of hospitals. These clusters vary significantly in their organization and communication strategies regarding climate protection and allocation of responsibilities, indicating that German hospitals prioritize climate protection to varying degrees. The findings suggest that employee engagement depends on how hospital administrative leaders organize and communicate the topic of climate protection in their institutions. The study underscores the importance of strategic leadership for climate protection in hospitals.
Learning temporal granularity with quadruplet networks for temporal knowledge graph completion
Infection with the entomopathogenic nematodes Steinernema alters the Drosophila melanogaster larval microbiome
The fruit fly Drosophila melanogaster is a vital model for studying the microbiome due to the availability of genetic resources and procedures. To understand better the importance of microbial composition in shaping immune modulation, we can investigate the role of the microbiota through parasitic infection. For this, we use entomopathogenic nematodes (EPN) of the genus Steinernema which exhibit remarkable ability to efficiently infect a diverse array of insect species, facilitated by the mutualistic bacteria Xenorhabdus found within their gut. To examine the microbiome changes in D. melanogaster larvae in response to Steinernema nematode infection, D. melanogaster late second to early third instar larvae were exposed separately to S. carpocapsae and S. hermaphroditum infective juveniles. We have found that S. carpocapsae infective juveniles are more pathogenic to D. melanogaster larvae compared to the closely related S. hermaphroditum. Our microbiome analysis also indicates substantial changes in the size and composition of the D. melanogaster larval microbiome during infection with either nematode species compared to the uninfected controls. Our results serve as a foundation for future studies to elucidate the entomopathogenic-specific effector molecules that alter the D. melanogaster microbiome and understand the role of the microbiome in regulating insect anti-nematode immune processes.
Reliability inference for multi-component stress-strength systems with heterogeneous Lomax-distributed components under progressive censoring
Effects of access to a well-resourced environment on dairy calf cognition and affective state
Dairy calves are often raised without maternal contact and in environments of low complexity. Environments that limit natural behaviors are known to impair cognitive development and affective states. We explored the effect of environmental complexity on one measure of social cognition (the ability to discriminate between conspecifics) and one measure of affective states (sensitivity to reward). Pairs of calves were randomly allocated to either 1) pair housing for 22.5 h/d with 1.5 h of daily access to a well-resourced pen which included 3 other calves and physical devices (Enriched; n = 6 pairs) or, 2) pair housing for 24 h/d (Control; n = 6 pairs). Calves were trained to discriminate between 2 calves in a Y-maze. Twelve of the 24 calves tested met the learning criterion, requiring 15.7 ± 2.59 (mean ± SD) training sessions. Treatment did not affect the number of sessions needed to reach the learning criterion. Calves were then subjected to a Successive Negative Contrast test during which they were trained to approach a 0.5 L milk reward over 3 trials/day for 3 days. On the last training day, latencies of enriched calves increased over daily trials while latencies for control calves were lower and remained relatively consistent, indicative of greater sensitivity to reward. Starting on day 4, the reward was reduced to 0.1L of milk/trial and remained at this level for the next 5 test days. Latency to reach the reward increased across trials within each test day, but no effect of treatment or test day was found. Our findings suggest that calves can discriminate among individuals but learning was not affected by treatments. Calves raised in standard pair housing showed increased sensitivity to reward, consistent with experiencing a more negative emotional state in comparison to calves reared with access to a well-resourced environment.
STAU1 exhibits oncogenic characteristics and modulates alternative splicing and gene expression in lung adenocarcinoma cells
Signal mining and analysis of adverse events of Brentuximab Vedotin base on FAERS and JADER databases
Objectives Brentuximab Vedotin (BV) is a novel antibody-drug conjugate (ADC) approved for the treatment of classical Hodgkin’s lymphoma and systemic anaplastic large cell lymphoma. However, as a relatively new therapeutic agent, the long-term safety profile and adverse event (AE) profile of BV require further investigation. This study aimed to identify significant and unexpected AEs associated with BV using data from the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) databases. Methods Data on BV-related AEs were extracted from the FAERS and JADER databases. Signal detection was performed using the reporting odds ratio (ROR) and 95% confidence intervals (95% CI). Risk signals were categorized according to system organ classes (SOCs) and preferred terms (PTs) as defined by the Medical Dictionary for Regulatory Activities (MedDRA) version 26.0. In addition, the onset times of BV-related AEs were analyzed. Results Between 2004 and 2023, a total of 19,279 and 2,561 AEs related to BV were recorded in the FAERS and JADER databases, respectively. At the SOC level, prominent signals in the FAERS database included blood and lymphatic system disorders, benign, malignant, and unspecified neoplasms (including cysts and polyps), as well as congenital, familial, and genetic disorders. In the JADER database, the most notable signals involved benign, malignant, and unspecified neoplasms, blood and lymphatic system disorders, and nervous system disorders. At the PT level, the top five signals in the FAERS database were peripheral motor neuropathy, peripheral sensory neuropathy, pneumocystis jirovecii pneumonia, febrile bone marrow aplasia, and polyneuropathy. Unexpected AEs included febrile bone marrow aplasia and Guillain-Barré syndrome. In the JADER database, the top five signals included peripheral motor neuropathy, peripheral sensory neuropathy, bacterial gastroenteritis, febrile neutropenia and pneumonia, with unexpected AEs such as left ventricular dysfunction, cardiomegaly, retinal detachment, and marasmus. The median onset time of AEs was 22 days (interquartile range [IQR] 7–81 days) in FAERS and 27 days (IQR 7–73 days) in JADER. Conclusion The signal detection results from the FAERS and JADER databases highlight the importance of monitoring significant and unexpected AEs associated with BV, particularly in the early stages of treatment. These findings contribute to enhancing the post-marketing safety profile of BV and offer valuable insights for clinical risk management strategies.
The contribution of oral infectious diseases in lacunar stroke based on meta-analysis and Mendelian randomization study
Minocycline inhibits rosacea-like inflammation through the TLR4-mediated NF-κB signaling pathway in vivo and in vitro
Background Rosacea is a chronic inflammatory skin disease characterized by multiple intricate pathogenic factors. Previous studies have substantiated the anti-inflammatory properties of minocycline and its potential therapeutic efficacy in treating rosacea. However, further elucidation of the underlying mechanism is warranted. Methods HaCaT cells and BALB/c mice were treated with LL37. Moreover, the effect of minocycline on rosacea was explored through the addition of an NF-κB inhibitor (PDTC) or overexpression of Toll-like receptor 4 (TLR4). The expression of related markers was detected by western blotting, immunofluorescence, ELISA, flow cytometry, etc. Results Minocycline suppressed dermal infiltration of inflammatory cells in rosacea-like mice and reduced the expression of inflammatory cytokines in rosacea-like mice and cells. Moreover, minocycline downregulated the expression of TLR4 and p-NF-κB thereby inhibiting ROS production. However, overexpression of TLR4 or the addition of PDTC counteracted the effects of minocycline by promoting cellular inflammation and ROS production. Mechanistically, minocycline hinders TLR4/TNF-α activation induced by LL37 in skin and cells to suppress the expression of inflammatory cytokines. Conclusion Minocycline alleviates inflammation progression in rosacea by downregulating TLR4 and inhibiting the activation of the NF-κB pathway, providing a scientific basis for subsequent clinical treatment.
Sustainable prospective proposals for utilizing modifiers in bitumen industry to address global warming
Incidence and predictors of cardiovascular disease mortality and all-cause mortality in patients with type II diabetes with peripheral arterial disease
Objective This cohort study estimated the incidence and predictors of cardiovascular disease (CVD) and all-cause mortality among patients with type 2 diabetes mellitus (T2DM) and various stages of peripheral arterial disease (PAD) at the largest tertiary referral hospitals in upper-northern Thailand. Methods This study recruited 278 T2DM and PAD patients for a 7-year cohort study. These patients completed health questionnaires and underwent physical examinations including ankle-brachial index measurements and clinical assessment to determine PAD severity. Mortality endpoints were determined using hospital death registers and national death records. The Cox proportional hazards and subdistribution hazard models were used to estimate PAD’s effect on mortality, quantifying the association with hazard ratios (HR) and subdistribution hazard ratios (SHR). Results PAD patients were categorized into three subgroups. Over seven years, the cumulative all-cause mortality rate was 36%, or 6.4 deaths per 100 person-years. Multivariable analysis revealed critical limb ischemia (CLI) patients had significantly higher risks of all-cause (HR 5.26, 95%CI 3.10–8.94) and CVD mortality (SHR 6.20, 95%CI 3.20–12.03) compared to their asymptomatic peers. No statistically significant differences in non-CVD mortality were noted across PAD subgroups. Conclusion CLI, chronic kidney disease, and underweight (body mass index < 18.5 kg/m2) emerged as independent mortality predictors. Conversely, asymptomatic PAD patients had a similar overall mortality risk as those with intermittent claudication. These findings highlight the need for risk stratification and patient empowerment to optimize management of these complex conditions.
Angiogenesis related gene signatures predict prognosis and guide therapeutic strategies in renal clear cell carcinoma
Factors associated with female infertility in Ethiopia: A systematic review and meta-analysis
Background Infertility is a significant public health issue that affects couples worldwide. The impacts of infertility is notably higher in Ethiopia due to various factors, such as cultural stigmas surrounding infertility and inadequate infrastructure for diagnosis and treatment. Several fragmented primary studies have assessed factors associated with female infertility in Ethiopia; however, their findings have been controversial and inconclusive. This meta-analysis aimed to identify the factors associated with female infertility in Ethiopia. Materials and methods A comprehensive search was conducted across multiple databases and search engines, including PubMed, African Journals Online, EBSCO, Google Scholar, and the Directory of Open Access Journals. Additionally, studies were searched from the institutional repositories of Ethiopian universities. Data analysis was performed using Stata version 17. The quality of the included studies was evaluated using the Newcastle-Ottawa quality assessment instrument. Heterogeneity and publication bias were assessed using I² and Egger’s tests, respectively. A random effects model was employed to identify factors associated with female infertility. The PROSPERO registration number for this meta-analysis was CRD42024525437. Result Six studies were included in the analysis. Factors associated with female infertility included having multiple sexual partners (odds ratio [OR] = 4.31, 95% confidence interval [CI] = 3.46–5.16), a history of sexually transmitted diseases (OR = 2.76, 95% CI = 1.61–3.91), alcohol-abusing partners (OR = 1.57, 95% CI = 1.25–1.89), Khat-abusive partners (OR = 1.85, 95% CI = 1.36–2.35), and women’s age over 35 years (OR = 2.07, 95% CI = 1.32–2.81). Conclusion Having multiple sexual partners, a history of sexually transmitted diseases, an alcohol-abusing partner, a khat-abusing partner, and being over the age of 35 were significantly associated with female infertility in Ethiopia. Addressing these risk factors through education, early intervention, lifestyle modifications, and partner involvement can help reduce the burden of infertility and improve the chances of successful conception. The findings underscore the need for further research on understudied factors contributing to female infertility in Ethiopia, including immune function, psychological health, environmental exposures, as well as endocrinological and gynecological conditions.
In vitro activity of cefiderocol against Gram-negative aerobic bacilli in planktonic and biofilm form–alone and in combination with bacteriophages
Abstract Multi-drug resistant Gram-negative pathogens are increasingly difficult-to-treat perpetrators of infections. New, innovative, and more multifaceted therapies for the treatment of multi-drug resistant strains are thus urgent to hinder further drug resistance and mitigate deadly, untreatable infections. Our study aimed to investigate the efficacy of cefiderocol against Gram-negative aerobic bacteria alone and in combination with phages. The minimum inhibitory concentration (MIC) of cefiderocol was determined using the microdilution broth method, while the minimum biofilm bactericidal concentration was assessed using isothermal microcalorimetry. The combined effect of cefiderocol and phages was evaluated using colony-forming unit counts. Results demonstrated a notable antibacterial effect of cefiderocol, with 83.4% of tested strains exhibiting susceptibility. When combined with phages, the MIC of cefiderocol was reduced by 2–64-fold, indicating a synergistic interaction between the two agents. Furthermore, the combination therapy showed enhanced efficacy against biofilm compared to monotherapy with either cefiderocol or phages alone, leading to complete biofilm elimination in certain cases. This study highlights the potential of combining cefiderocol with phages as a strategy to combat multi-drug resistant Gram-negative bacterial infections. The observed synergy suggests that this combination therapy could improve treatment outcomes and help address the challenges of antibiotic resistance and biofilm-associated infections.
Outpatient care changes and associated mortality among Veterans with heart failure during the COVID-19 pandemic
Background The mortality risk associated with loss of in-person outpatient visits or transition to virtual care in patients with heart failure (HF) during the COVID-19 pandemic is unknown. Objectives Assess changes in outpatient HF care patterns and associated mortality. Methods Retrospective analysis of HF patients using national Veterans-Health-Administration (VHA) data. Among 509,511 HF patients who received VHA care, we compared mean monthly days-with-an-outpatient-visit from 2/2018–1/2020 (pre-COVID) versus 2/2020–1/2021 (COVID) using T-tests. In a subset of 321,439 patients with ≥1 VHA cardiology or primary-care visit in 2019, we related the presence and type of outpatient visit with mortality using Cox-Regression estimated hazard-ratios (HRs). Results Despite a 2–3-fold increase in video-only visits and use of telephone visits to maintain access, the overall days with outpatient visits decreased from a monthly-average of 81.4 ± 6.1 in 2018–2019 and 81.0 ± 5.6 in 2019–2020, to 57.8 ± 11 days in 2020–2021 (P < 0.01 for both), per 100 Veterans. When compared to patients with no-visits during the study period, the adjusted-mortality risk was lowest for patients with at least one in-person (HR 0.42, 95%CI: 0.41–0.44), followed by video-only (HR 0.52, 95%CI: 0.50–0.55) and then telephone-only (HR 0.57, 95%CI: 0.54–0.60) visits (p = 0.14 for trend). Results remained similar when the analysis was repeated (without including telephone visits) for pre-COVID (2/2018–1/2020) periods. Conclusions Despite an increase in video and use of telephone visits during the COVID-19 pandemic, there was still a decrease in total outpatient visits for patients with HF. The presence and type of outpatient encounter was associated with the adjusted risk of mortality.
An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization
Abstract Leukemia is a common type of blood cancer marked by the abnormal and uncontrolled proliferation and expansion of white blood cells. This anomaly impacts the blood and bone marrow, diminishing the bone marrow’s capacity to generate platelets and red blood cells. Abnormal red blood cells in the bloodstream harm various organs, such as the kidneys, liver, and spleen. Detection and classification of infected patients at an early stage can save their lives. In this paper, a new Artificial Intelligence (AI) system is proposed. The proposed system is called Leukemia Classification System (LCS). The proposed LCS composed of five stages, which are; (i) Image Processing Stage (IPS), (ii) Image Segmentation Stage (ISS), (iii) Feature Extraction Stage (FES), (iv) Feature Selection Stage (FSS), and (v) Classification Stage (CS). During IPS, the input images are preprocessed through several processes: resizing, enhancement, and filtering. Next, the preprocessed images are segmented through ISS. Then, two types of features, texture and morphological features, are extracted. We feed these extracted features to FSS, which uses a proposed method to select the most important and effective features. The proposed method is called the Dimensional Archimedes Optimization Algorithm (DAOA). DAOA is based on the Archimedes Optimization Algorithm (AOA) and Dimensional Learning Strategy (DLS). Actually, DLS transmits valuable information about the ideal position of the population in every generation to the personal best position of each individual particle. This improves both the precision and efficiency of convergence while reducing the likelihood of the “two steps forward, one step back” phenomenon. This problem offers a more precise solution. Finally, these selected features are fed to the proposed classification model. Experimental results show that the proposed LCS outperforms the others.
Analysis of government subsidy strategies in the supply chain of science and technology innovation platform
In the current era of technological advancements and intense global competition, innovation in Science and Technology (S&T) has become a crucial aspect of every country’s development strategy, and the development of cutting-edge technologies in core areas is of utmost importance. Innovation platforms that offer services for core technologies can be classified into three operational models: Public Welfare Platforms, Social Enterprise Platforms, and Commercial Platforms, depending on their objectives. To accelerate the realization of S&T innovation, the government provides subsidies to guide the S&T innovation platform and increase the participation of innovation subjects. With the help of optimization theory and supply chain theory, this paper constructed a model employing the game approach to delve into the pricing strategies of S&T platforms under three distinct models: Public Welfare Platform, Social Enterprise Platform, and Commercial Platform. The paper also discusses the effects of government subsidies on different innovation subjects. The study demonstrates that all three operational models can achieve the optimal membership fee for research users and the optimal commission rebate paid by the platform to resource providers. In the context of the Public Welfare Platform, despite the highest social welfare, the platform’s profit remains negative. Consequently, price subsidies are required for research users. At this juncture, the government subsidies to the S&T innovation platform yield the greatest social welfare yet the lowest profit for the platform. In contrast, the Social Enterprise Platform entails government subsidies for resource providers and research users, which can enhance the platform’s profit. The effect of subsidizing the resource providers on the profit growth of the S&T innovation platform is more significant than the effect on the improvement of social welfare. In the context of the Commercial Platform, regardless of the existence of government subsidies, the growth of social welfare and platform profits with the platform service level can be achieved. Furthermore, government subsidies for scientific research users are the most effective. These results provide theoretical and practical lessons for the pricing of the S&T innovation platform and the subject of government subsidies.
Age-invariant benefits of spatiotemporal predictions amidst distraction during dynamic visual search
Abstract Visual search tasks are widely used to study attention amidst distraction, often revealing age-related differences. Research shows older adults typically exhibit poorer performance and greater sensitivity to distraction, reflecting declines in goal-driven attention. However, traditional search tasks are static and fail to capture the challenges and opportunities in natural environments, which include predictive structures within extended contexts. We designed a search variation where targets and distractors compete over time and embedded spatiotemporal regularities afford prediction-led guidance of attention. Critically, we manipulated the number of distractors to chart how benefits of expectations and deficits from distraction varied with age. Younger and older adults searched for multiple targets as they faded in and out of the display while varying the number of distracting elements between trials. Half the targets appeared at the same time and approximate locations and could be predicted. While we found evidence for decrement and elevated sensitivity to distraction with increasing age, benefits from predictions occurred in all groups. Interestingly, regardless of age, effects of predictions were only significant during periods of high distraction. This work extends our understanding of attention control through ageing to dynamic settings and indicates a dissociation between goal-directed and learning-driven attentional guidance.
Multivariable prediction of early malignancy detection among first-degree relatives of patients with hereditary colorectal cancer based on the health belief model: A cross-sectional study from the largest hereditary colorectal cancer cohort in China
Background First-degree relatives (FDRs) of hereditary colorectal cancer are at an increased risk of cancer and receiving early detection and surveillance on malignancies is an efficacious strategy to reduce cancer-related morbidity and mortality. But there is rare information on screening in this cancer high-risk group in China. Objective The aim of this study was to explore the detection and surveillance on malignancies and its predictors among FDRs of patients with hereditary CRC based on the largest hereditary colorectal cancer cohort from China. Methods We conducted an exploratory, descriptive, cross-sectional study. 530 FDRs were recruited from December 2021 to December 2022, evaluated using the questionnaire on knowledge, attitudes and behavior of malignancies early detection and the Champion’s Health Belief Model Scale. The main outcome were identified using logistic regression analysis. Results Among all the FDRs, only 122 (23.0%) underwent malignancies early detection. The predictors of malignancies early detection included knowledge score (OR = 1.117, P < 0.001), sex (OR = 0.244, P < 0.001), age (OR = 4.627, P < 0.001), marital status (OR = 3.815, P < 0.001), chronic disease history (OR = 2.945, P < 0.01), diagnosis of index patient (OR = 2.876, P < 0.001), attitude about “cancer is preventable” (OR = 3.405, P < 0.05) and “one needs malignancies early detection even if feel healthy” (OR = 16.477, P < 0.001), perceived susceptibility (OR = 1.106, P < 0.05) and self-efficacy (OR = 1.244, P < 0.001). Conclusion The uptake of malignancy early detection among FDRs should be improved. Some demographic and health-related characteristics, knowledge score, perceived susceptibility and self-efficacy were the most important predictors of malignancies early detection. Enhancing the recognition of clinical features of hereditary CRC and offering personalized genetic counseling, along with tailored cancer risk assessments, could further optimize individual cancer surveillance and prevention strategies, helping to reduce the risk of malignancies in high-risk populations.