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High prevalence of multi-drug-resistant bacteria in faecal samples from UK passerine birds

Abstract Wild birds are a near ubiquitous sight in gardens, offering pleasure to many people through supplementary feeding, song, or other interactions. However, they are also potential carriers of many bacteria, including  Campylobacter spp.,  Salmonella spp. , Enterococcus spp., and  E. coli ; some of these may be resistant to commonly used drugs. This study collected faecal samples from multiple species of UK passerine birds, isolating bacterial pathogens to assess carriage and drug resistances associated with those bacteria. 75% of birds were carrying at least one bacterial species which was multi drug resistant (MDR; resistant to three or more classes of antimicrobial), with 11.6% of birds carrying Salmonella spp., 18.9% carrying  Campylobacter  spp., 78% carrying  Enterococcus  spp., and all carrying E. coli  strains. Many of these strains were shown to be MDR with 70%, 88%, 32% and 59% respectively. Intercontinental migration was shown to be a risk factor for carriage of many of the pathogens, as was an associated with human habitation. Age was also a risk factor with younger birds twice as likely to carry  Campylobacter  spp. than adults, and house sparrows ( Passer domesticus ) and blackbirds ( Turdus merula ) being particularly high-level carriers compared to other species. The high-level carriage and shedding of MDR  E. coli  and other zoonotic pathogens within the faecal samples of multiple species of passerine birds offers a timely reminder of the risks which these bacteria, and their drug resistance profiles may pose to human and animal health in the UK and worldwide. It also shows a level of high environmental contamination, which birds may continue to contribute towards, until our use of antimicrobials, and level of drug-resistant bacteria is decreased. Developing mechanisms for reducing levels of carriage of MDR bacteria in wild bird populations through, for example, increased hygiene around bird feeding practices, may be key in reducing environmental contamination.

Navigating life when a loved one’s (euthanasia) death is near: A narrative interview study from the Netherlands

In this article, we describe our empirical research that started out as an exploration of “family involvement” in the Dutch practice of euthanasia, in the broader context of end-of-life decision-making and care under guidance of GPs in the home-setting. Informed by care-ethical insights and narrative approaches to qualitative research, we performed an in-depth interview study with 18 close relatives of people with incurable metastasized cancer (9 prospective, 9 retrospective). We came to understand how relatives’ involvement—not only in euthanasia but in any mode of dying—cannot be separated from relatives’ efforts to navigate through many dimensions of life when the death of loved one is near. Relatives have to navigate different spaces, decision-dynamics and the unfolding of professional care, strong emotional experiences, and intimate relationships (“the I-you-we”). This study brought to the fore that relatives and patients first and foremost accompany each other on this journey. The role of the GP can be valuable but vulnerable, and relatives’ broader social network and other professionals can be of enormous importance. The results of this study invited us to shift our perspective: it is not family members who get involved in euthanasia as a primarily medical affair. Instead, medical professionals are taking part in the profoundly social-relational experience of death and dying within families, whether that entails euthanasia or not. With this shift of perspective, specific practical and ethical questions start to receive more attention, for example questions about the available support for both patients and relatives regardless of the mode of dying.

GLP-1-based therapies for diabetes, obesity and beyond

Prevalence and associated factors of vitreoretinal interface disorders using multicolour OCT among Chinese population in Fujian eye study

Global burden of major chronic respiratory diseases among older adults aged 55 and above from 1990 to 2021: Changes, challenges, and predictions amid the pandemic

Objective To characterize sex- and age-specific changes in the comprehensive burden of major chronic respiratory diseases (CRDs) and their attributable risk factors among adults aged ≥55 years globally, regionally, and nationally from 1990 to 2021 using the Global Burden of Disease (GBD) 2021 database. Methods Utilizing the GBD 2021 database, we performed in-depth analyses and preliminary projections of global, regional, and national burden trends for chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung disease & pulmonary sarcoidosis (ILD&PS) through multi-model approaches including but not limited to Age-Period-Cohort (APC) models, Joinpoint regression, and Bayesian Age-Period-Cohort (BAPC) modeling. Results The overall global CRD burden among adults ≥55 years declined from 1990 to 2021. However, the Corona Virus Disease 2019 (COVID-19) pandemic differentially altered asthma and COPD prevalence and incidence trends globally: low Socio-demographic Index (SDI) regions experienced an accelerated increase in prevalence, while high SDI regions showed a steeper rise in incidence. High mortality and disability-adjusted life years (DALYs) rates remained concentrated in low-middle SDI regions, notably Asia and North America. Consequently, prevalent CRD cases in this age group reached 223 million (95% UI 206.5–241.5) in 2021—accounting for half of all-age cases—with 18.47 million incident cases (95% UI 16.97–20.11), causing 4.15 million deaths (95% UI 3.76–4.58) and 83.67 million DALYs (95% UI 77.49–90.36). Air pollution, smoking, obesity, and chronic cold exposure persistently influenced COPD and asthma prevalence across regions and sexes. Conclusion The pandemic shifted global CRD burden trends, particularly for asthma followed by COPD. Concurrent with global aging, burden trajectories across SDI levels raise concerns. As COVID-19 becomes endemic, older adults will experience impacts from recurrent viral infections, increasingly manifesting in coming years.

Endovascular structures of the basilar artery as forms of the basilar nonfusion spectrum

Design and numerical simulation of a semi-cast-in-situ synthetic material sports surface on shock absorption performance optimization

The persistent issue of moisture-induced “heave” in synthetic sports surfaces can affect athlete safety and surface performance. The objective of the study was to design an innovative synthetic material athletic track structure that mitigates adhesive failure between the synthetic layer and the cement base due to underground moisture. The new structure ensures field safety and meets biomechanical requirements for performance and shock absorption. Numerical simulation methods are employed to analyze the shock absorption performance of the synthetic material track and field facility, incorporating the new structure, and subsequently to propose optimization strategies for the structural design. The optimal structure adopted a circular casting hole design, with a cast-in-situ surface layer thickness of 12 mm, a prefabricated surface layer thickness of 6 mm, a hole diameter of 45 mm, and a hole spacing of 80 mm arranged in a square pattern. The results indicated that the proposed structure not only met the required standards for shock absorption, but also offered a promising solution to the issue of moisture-induced “heave” prevalent in traditional sports surfaces. The study provided important theoretical support and practical guidance for the scientific and efficient construction of athletic tracks.

Structurally optimized SiC CMOS FinFET for high-temperature and low-power SoC logic integration

The relationship between lactate/albumin ratio and prognosis in children with acute kidney injury

Acute kidney injury (AKI) is a prevalent and critical complication in pediatric patients, severely influencing both survival outcomes and quality of life. This research seeks to evaluate the prognostic value of the lactate/albumin ratio (LAR) in predicting short-term outcomes for children diagnosed with AKI. A retrospective analysis was conducted using data from the Pediatric Intensive Care database, covering the period from 2010 to 2018. The relationship between the LAR and in-hospital mortality was explored using smoothing curve fitting, multivariate logistic regression, and Kaplan-Meier survival analysis. Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive capability of LAR for in-hospital mortality. A total of 631 pediatric patients with AKI were included in this study. Smoothing curve fitting revealed a nearly linear positive association between the LAR and in-hospital mortality. Multivariate logistic regression analysis identified LAR as an independent predictor of in-hospital mortality (OR 2.58, 95% CI 1.85–3.59). Kaplan-Meier survival analysis showed that patients with a higher LAR had a significantly greater 30-day mortality rate compared to those with lower LAR values. ROC curve analysis demonstrated an area under the curve of 0.85 (95% CI 0.80–0.90). At the optimal cutoff value of 0.80, the sensitivity was 80.42%, specificity was 77.97%, and accuracy was 80.19%. These results suggest that LAR is a promising prognostic indicator of mortality in pediatric patients with AKI, and could serve as an early indicator of risk stratification.

The unexplored mechanism of antitumoral effect of pirfenidone in melanoma cells

Dairy cow performance is associated with longitudinal microRNA profiles

Modern high producing dairy cows are still affected by poor fertility and disease, despite improvements achieved through genetic selection programs. Additional biomarkers of health and performance traits in cattle could enhance animal welfare and profitability by allowing farmers to cull animals before problems occur. We performed pilot investigations of plasma microRNA (miRNA) profiles during early life as potential biomarkers associated with future performance in dairy cows. The latter included survival to two years of age, age at first calving, yield of milk, fat and protein, mastitis and lameness traits, conception rate, number of services per conception, and calving interval. Using qPCR, we obtained longitudinal measurements and ratios involving nine miRNAs (miR-126-3p, miR-127, miR-142-5p, miR-154b, miR-27b, miR-30c-5p, miR-34a, miR-363, miR-425-3p) in plasma samples from three age groups: calves (<1 month), heifers (14–23 months), and first lactation cows (29–35 months). Changes in miR-126-3p from calf to first lactation cow were associated with first lactation milk yield and second lactation milk somatic cell count (an udder health indicator). Moreover, the miR-127 to miR-30c-5p ratio in cows was associated with milk fat and protein yield in the first two lactations, whereas miR-142-5p levels and several miRNA ratios involving this miRNA, were associated with second calving interval (a cow fertility trait). Our results identified novel early life biomarkers that warrant further investigation to determine whether they may predict dairy cattle performance.

Medication adherence among children with heart failure at the University of Gondar Comprehensive Specialized Hospital Gondar Northwest Ethiopia

An optimized stacking-based TinyML model for attack detection in IoT networks

With the expansion of Internet of Things (IoT) devices, security is an important issue as attacks are constantly gaining more complex. Traditional attack detection methods in IoT systems have difficulty being able to process real-time and access limitations. To address these challenges, a stacking-based Tiny Machine Learning (TinyML) models has been proposed for attack detection in IoT networks. This ensures detection efficiently and without additional computational overhead. The experiments have been conducted using the publicly available ToN-IoT dataset, comprising a total of 461,008 labeled instances with 10 types of attacks categories. Some amount of data preprocessing has been done applying methods such as label encoding, feature selection, and data standardization. A stacking ensemble learning technique uses multiple models combining lightweight Decision Tree (DT) and small Neural Network (NN) to aggregate power of the system and generalize. The performance of the model is evaluated by accuracy, precision, recall, F1-score, specificity, and false positive rate (FPR). Experimental results demonstrate that the stacked TinyML model is superior to traditional ML methods in terms of efficiency and detection performance, and its accuracy rate is 99.98%. It has an average inference latency of 0.12 ms and an estimated power consumption of 0.01 mW.

The aryl hydrocarbon receptor: a rehabilitated target for therapeutic immune modulation

Association between autonomic dysfunction and arterial stiffness in hypertensive patients

Leaf disease detection and classification in food crops with efficient feature dimensionality reduction

Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. Bidirectional LSTM (BiLSTM) networks store data in both forward and backward directions, while traditional Long Short-Term Memory (LSTM) networks handle data in a specific order. This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. Utilizing CNNs for feature extraction and BiLSTM networks for temporal dependency capture, the method incorporates label information as constraints, leading to more discriminative features for disease classification. Tested on datasets of pepper and maize leaf images, the method achieved a 99.37% classification accuracy, outperforming existing dimensionality reduction techniques. This cost-effective approach can be integrated into precision agriculture systems, facilitating automated disease detection and monitoring, thereby enhancing crop yields and promoting sustainable farming practices. The proposed Efficient Labelled Feature Dimensionality Reduction utilizing CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) model is compared to current models to show its effectiveness in reducing extracted features for leaf detection and classification tasks.

Transparent brain tumor detection using DenseNet169 and LIME

Abstract A crucial area of research in the field of medical imaging is that of brain tumor classification, which greatly aids diagnosis and facilitates treatment planning. This paper proposes DenseNet169-LIME-TumorNet, a model based on deep learning and an integrated combination of DenseNet169 with LIME to boost the performance of brain tumor classification and its interpretability. The model was trained and evaluated on the publicly available Brain Tumor MRI Dataset containing 2,870 images spanning three tumor types. Dense169-LIME-TumorNet achieves a classification accuracy of 98.78%, outperforming widely used architectures including Inception V3, ResNet50, MobileNet V2, EfficientNet variants, and other DenseNet configurations. The integration of LIME provides visual explanations that enhance transparency and reliability in clinical decision-making. Furthermore, the model demonstrates minimal computational overhead, enabling faster inference and deployment in resource-constrained clinical environments, thereby highlighting its practical utility for real-time diagnostic support. Work in the future should run towards creating generalization through the adoption of a multi-modal learning approach, hybrid deep learning development, and real-time application development for AI-assisted diagnosis.

Signed log-likelihood ratio test for the scale parameter of Poisson Inverse Weibull distribution with the development of PIW4LIFETIME web application

The three-parameter Poisson Inverse Weibull (PIW) distribution offers enhanced flexibility for modeling system failure times. This study introduces the signed log-likelihood ratio test (SLRT) for hypothesis testing of the scale parameter (ω) in the PIW distribution and compares its performance with the test based on the asymptotic normality of maximum likelihood estimators (ANMLE). Simulation studies show that the SLRT consistently maintains type I error rates within the acceptable range of 0.04 to 0.06 at a significance level of 0.05, satisfying Cochran’s criterion across various sample sizes and parameter configurations. In contrast, the ANMLE method tends to be conservative, often underestimating the nominal significance level. In terms of empirical power, the SLRT outperforms the ANMLE, particularly in small-sample scenarios (n = 10, 15), and maintains superior power across all tested configurations. For example, when testing H0:ω=0.25 against H1:ω=0.5 with β=0.5,λ=1, and n = 10, the SLRT achieves a power of 0.6621, compared to 0.4181 for the ANMLE, demonstrating the SLRT’s robustness and reliability in limited-data. Moreover, the ANMLE generally exhibits low power in most cases, indicating reduced sensitivity to detecting true effects in small samples. However, with medium and large sample sizes (n = 30, 50, 80 and 100), the power of the ANMLE begins to approach that of the SLRT. Despite this, the ANMLE never outperforms the SLRT, highlighting a fundamental limitation of this method. Additionally, varying the shape parameter β while fixing λ=1 showed a negligible impact on power, further confirming the robustness of the SLRT. Sensitivity analyses also validate the reliability of the SLRT under extreme values of ω and across different sample sizes. To support practical application, the PIW4LIFETIME web application (accessible at https://jularatchumnaul.shinyapps.io/PIW4LIFETIME/) was developed to enable users to assess whether data fit the PIW distribution, estimate model parameters using maximum likelihood, and perform two-sided test for the scale parameter using SLRT. The performance of the proposed method and the PIW4LIFETIME web application was demonstrated through a real-world example.

Village and age based precision mapping of schistosomiasis and soil-transmitted helminths in Chevakadzi ward of Shamva district in Zimbabwe

Memory distrust and imagination inflation: A registered report

Imagination inflation occurs when the subjective confidence of a person that an event has occurred increases after they imagine it occurring. In this project, our primary aim was to test whether memory distrust is related to the imagination inflation effect in people who are aware of the discrepancies between their own memories and what they have imagined. Our secondary purpose was to investigate whether the influence of memory distrust on imagination inflation is moderated by traits that are described as disengagement from reality and to test whether memory distrust mediates the relationship between self-esteem and imagination inflation. In a three-step procedure, participants (N = 279) assessed their confidence that a list of childhood events occurred to them; then they imagined three of these events and reassessed their confidence. Half of the participants were subjected to a memory distrust induction procedure. To sensitize participants to discrepancies between actual childhood memories and imagined ones, some of them received cues about the source and/or perspective of the imagined events. Memory distrust as an individual trait was found to be unrelated to the imagination inflation effect. Furthermore, the expected effect of memory distrust as a state on susceptibility to the imagination inflation effect in groups sensitized to discrepancies was not confirmed. Therefore, it seems that people who we consider to be distrustful of their memory are no more susceptible to this type of memory distortion than memory trusters.