Association between unclean cooking fuel use and hearing problems among adults aged ≥65 years, a cross-sectional study

Background and Aims: Literature suggests that outdoor air pollutant exposure is associated with hearing problems, but examination of this link has not extended to any potential association between hearing ability and the use of unclean cooking fuels. The current paper investigates whether such a link exists, utilizing a large sample of older adults from low- and middle-income countries (LMICs) where such fuels are commonly used.Methods: Data from the Study on global AGEing and adult health (SAGE) were analyzed. This is a nationally representative and cross-sectional data set collected for the World Health Organization for residents of South Africa, China, Ghana, India, Mexico, and Russia. A range of “unclean” cooking fuels were assessed, namely agriculture or crop, animal dung, coal or charcoal, Kerosene or paraffin, shrubs or grass, and wood. Hearing problems referred to the interviewer-rated presence of this condition. Statistical analysis was done using multivariable logistic regression.Results: The present work analyzed data from 14,585 individuals aged ≥ 65 years [mean (SD) age 72.6 (11.5) years; 55.0% females]. In the overall sample and in the final adjusted model, unclean cooking fuel use was associated with a significantly increased risk of hearing problems (OR = 1.68 (95% CI = 1.22–2.30). This association was significant for females (OR = 2.36; 95% CI = 1.53–3.63) but not for males (OR = 1.20; 95% CI = 0.79–1.81).Conclusion: Unclean cooking fuel use is associated with an increased risk of hearing problems among adult residents of LMICs over 65 years of age, particularly among females. Findings from this study support the development of Sustainable Development Goal 7 (United Nations), which advocates for fairer and more sustainable access to modern energy, as well as a means to prevent avoidable hearing problems.</p

Leveraging digital capabilities for ESG performance: the mediating roles of innovativeness and resilience in the UK healthcare sector

This study examines the extent to which digital business capabilities (DBCs) influence B2B firms' environmental, social, and governance (ESG) performance, and explores the mediating roles of business model innovativeness (BMI) and organisational resilience (OR) in this relationship. In addition, it investigates the moderating effects of absorptive capacity (AC) and market dynamism (MD), offering insights into how internal and external conditions shape the DBC–ESG performance linkage. Drawing on empirical data from managers in the UK healthcare sector, the study finds that while DBCs may not directly impact ESG outcomes, they play a crucial enabling role by positively influencing internal capabilities such as innovativeness and resilience. The direct DBC–ESG link is not significant in the pooled sample, but does emerge in the B2B healthcare context, while the most consistent pathway is the indirect effect through resilience. Furthermore, firms with higher levels of AC are better positioned to leverage digital capabilities for innovation. However, the positive effect of resilience on ESG performance is weakened under conditions of high MD. This study contributes to the literature on the drivers of ESG performance by elucidating how firms can manage ESG practices through the interplay of digital capabilities, innovation, and resilience.</p

Impact of animal socioecology on gut microbial communities: insights from wild meerkats in the Kalahari

The social organisation of animals likely shapes the composition, diversity and stability of microbiomes, giving rise to the concept of the ‘social microbiome’—microbial communities shared within and across social units, or ‘islands’, ranging from individuals to entire ecosystems. Understanding the connections and their underlying drivers is crucial for revealing how socioecology influences microbiomes and associated health outcomes. However, empirical assessments are still limited, and the relative influence of social organisation compared to intrinsic (biological) and extrinsic (environmental) factors in shaping microbiomes is particularly unclear. Here, we used a long‐term, individual‐based study of Kalahari meerkats ( Suricata suricatta ) to test predictions from the social microbiome concept. We assessed the relative influence of social factors, biological traits and environmental variables on gut microbial communities, while also accounting for the effects of microbial phylogenetic relatedness and within‐host associations or co‐occurrence independent of phylogeny. Meerkat microbiomes exhibited highly ‘nested’ and weakly ‘modular’ structures: individuals with lower diversity hosted amplicon sequence variants (ASVs) that were subsets of the overall community, though some bacterial taxa clustered distinctly among hosts. Microbiomes were more similar within social groups than between them. Group membership strongly influenced the co‐occurrence of many beneficial ASVs, as well as a few potentially harmful ones. This effect was stronger than that of kinship, though closer relatives shared more similar microbiomes within some groups. While a range of social, biological and environmental factors influenced bacterial abundance, group membership, individual age and sampling time since sunrise had the most significant impact. ASV‐ASV co‐occurrence within hosts, independent of phylogeny, also played a major role. In contrast, individual‐level social traits (e.g. dominance, immigration), other environmental (e.g. sampling temperature, rainfall, hours since foraging), demographic (sex) and health‐related factors (body condition, disease status) had weaker effects on bacterial abundance. We show that gut microbiomes are shaped by a combination of factors, highlighting the importance of separating the effects of social organisation from individual social traits, biological factors, environmental influences and microbe–microbe interactions. By identifying drivers of both beneficial and detrimental bacterial co‐occurrence, we provide a foundation for assessing how the social microbiome affects animal health and fitness.</p

A multi-task ensemble strategy for gene selection and cancer classification

Gene expression-based tumor classification aims to distinguish tumor types based on gene expression profiles. This task is difficult due to the high dimensionality of gene expression data and limited sample sizes. Most datasets contain tens of thousands of genes but only a small number of samples. As a result, selecting informative genes is necessary to improve classification performance and model interpretability. Many existing gene selection methods fail to produce stable and consistent results, especially when training data are limited. To address this, we propose a multi-task ensemble strategy that combines repeated sampling with joint feature selection and classification. The method generates multiple training subsets and applies multi-task logistic regression with ℓ2,1 group sparsity regularization to select a subset of genes that appears consistently across tasks. This promotes stability and reduces redundancy. The framework supports integration with standard classifiers such as logistic regression and support vector machines. It performs both gene selection and classification in a single process. We evaluate the method on simulated and real gene expression datasets. The results show that it outperforms several baseline methods in classification accuracy and the consistency of selected genes.</p

Increasing educational and workforce opportunity in areas of deprivation: tackling the inverse care law

More than fifty years after Tudor Hart’s identification of the Inverse Care Law [1], health equity in the UK remains elusive. The phenomenon of inverse care is now recognised globally [2], with growing evidence that equitable access to primary care is essential to reverse entrenched disparities. We argue that alongside the Inverse Care Law, two additional structural barriers — the Inverse Education Law [3,4] and the Inverse Workforce Law [5] — further constrain progress. Despite repeated policy commitments, the most deprived communities continue to face higher morbidity and mortality yet have fewer educational placements and fewer permanent healthcare staff. These shortfalls are compounded by heavy workloads, low morale, and poor retention. The evidence demonstrates a dose-dependent relationship between access to primary care and improved patient outcomes [6], yet provision remains skewed away from areas of greatest need. Education offers a critical lever for change. Placements in deprived areas, when well supported, both strengthen clinical capability and increase the likelihood of trainees choosing to remain in such communities [7]. Retention, however, requires targeted support to enable staff to flourish and to sustain their careers in high-need settings. In this paper, we outline the evidence that links inverse care, inverse education, and inverse workforce patterns. In a companion article, “Evaluation of a London-wide intervention targeted at tackling educational and workforce inequity in primary care workforce across London”, we present a five-year regional evaluation that operationalises these ideas. Taken together, these papers argue for a whole-systems approach to tackling health inequity by addressing inverse patterns in care, education, and workforce simultaneously.</p

Federated fuzzy C-Means for multi-layer network community detection in industrial Internet-of-things

Multi-layer network community detection is a crucial topic in Industrial Internet of things(IIoT). Due to communication and privacy requirements, network data is distributed across multiple devices, being a significant challenge to develop a model to learn latent information for community detection. To address the problem, this paper proposes a federated fuzzy C-Means for multi-layer network community detection. Firstly, non-negative matrix factorization is employed to obtain a low-dimensional representation via training local data in each client. The gradients of the global centroids are then transmitted to a central server for consistent fusion and complete community detection within the fuzzy C-Means framework. As a result, the training process for each client remains independent and leads to effectively privacy preservation. Experimental results demonstrate that the proposed method can successfully perform multi-layer network community detection across distributed devices and achieve comparable performance in contrast with centralized community detection methods on four public datasets.</p

The role of the collateral circulation in stable angina: an invasive placebo-controlled study

Background: Little correlation exists between the burden of ischemia and severity of angina in patients with stable coronary artery disease. This placebo-controlled, n-of-1 study investigated the relationship between ischemia, the collateral circulation, and symptoms in stable coronary artery disease. Additionally, it explored the association between progressive collateral recruitment and ischemic preconditioning.Methods: Fifty-one participants with severe single-vessel coronary artery disease and angina were recruited. Antianginal medications were stopped, and daily angina symptoms were documented using a dedicated smartphone application (ORBITA [Objective Randomized Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina] app) for 14 days before undergoing invasive pressure wire studies and coronary flow reserve assessment. Each participant then underwent four 60-s episodes of low-pressure balloon occlusion across their coronary stenosis. Each episode was paired with an audiovisually identical placebo inflation in a randomized order. After each episode, participants scored pain intensity on a 10-point scale, and a placebo-controlled pain intensity score was calculated. Collateral flow index was calculated from simultaneous measures of aortic, right atrial, and distal coronary wedge pressure during balloon occlusion. Higher Pr values from Bayesian models indicate a greater likelihood of association.Results: The mean (±SD) age of participants was 63±9 years, and 78% were men. The median (interquartile range) fractional flow reserve was 0.68 (0.57–0.79), the median instantaneous wave-free ratio was 0.80 (0.48–0.89), and the median coronary flow reserve was 1.42 (1.08–1.85). Daily angina frequency showed little correlation with severity of ischemia, as assessed by fractional flow reserve (Somers' D 0.124, Pr=0.057) or instantaneous wave-free ratio (Somers' D 0.056, Pr=0.150). However, there was strong evidence of an association between lower fractional flow reserve and instantaneous wave-free ratio values and greater collateral flow (Somers' D 0.302, Pr=0.998 and Somers' D 0.316, Pr=0.999, respectively). There was also strong evidence of an association between more collateralization (higher collateral flow index) and lower pain intensity scores (Somers' D 0.341, Pr=0.999). Finally, pain intensity scores and collateral flow index remained stable between sequential balloon occlusion episodes within individual patients, indicating little evidence of ischemic preconditioning.Conclusions: Coronary collateralization is associated with ischemic burden and may reduce the intensity of ischemic chest pain. This may explain the nonlinear relationship between stenosis, ischemia, and angina.</p

Effect of a Single Acupressure Treatment on the Mechanical Nociceptive Thresholds (MNTs) of the Equine Epaxial Back Musculature

Aims: Equine acupressure therapies possess an abundance of acclaimed anecdotal evidence; however, scientific validation remains limited. This investigation aimed to explore the effect of manual acupressure on the mechanical nociceptive thresholds (MNTs) of the equine epaxial musculature. Materials and Methods: The study design was a randomized, single crossover trial involving ten horses (five geldings and five mares) of various ages (16 ± 4.49 years). Horses were split into two groups and received a 10-minute acupressure or sham treatment. Nine acupressure points were selected and treated with 30 seconds of direct light pressure followed by six full circles. Each horse was assessed for points of sensitivity at three points bilaterally along the epaxial musculature, before, immediately after, and one day after the acupressure or sham treatment. A two-week washout period was implemented; the groups were reversed, and the protocol was repeated. Data were both parametric and nonparametric; therefore, to ascertain whether differences occurred in MNT values across the time points, a series of repeated measures ANOVAs and Friedman's analyses were undertaken. Where significant differences were found, post hoc Wilcoxon tests with Bonferroni correction identified how MNTs differed with time. Further paired t-tests or Wilcoxon rank tests determined whether differences occurred in the percentage of change between the treatment and control groups. Results: The results of the study suggest that acupressure elicits an immediate increase in MNTs in the epaxial musculature, most significantly at the thoracolumbar region. A decrease in this response could indicate lower sensitivity of the back, allowing better back kinematics and possibly improved performance.</p

Investigating the effect of reducing the signs and symptoms of lid wiper epitheliopathy in dry eye subjects with perfluorohexyloctane

Background: Perfluorohexyloctane (PFHO) acts to prevent the evaporation of the tear film. It has the potential to limit friction related issues between the eye lid margin and the ocular surface. Prior to the present work, this had not yet been evaluated.Objective: To examine the potential of using perfluorohexyloctane for reducing the signs and symptoms of lid wiper epitheliopathy (LWE).Methods: Data were collected at 4 visits spanning 2 months. Patients who had symptomatic dry eye and a LWE score of ≥1.0 on the Korb LWE scale were recruited. Participants were randomized to PFHO 4 times a day or no treatment. Lid wiper epitheliopathy was graded at each visit with the Korb and photographic LWE (PLWE) scales. Symptoms were assessed using the Standard Patient Evaluation of Eye Dryness questionnaire and visual analog scales (0–100).Results: A total of 52 participants were enrolled (mean ± SD age, 49.7 ± 15.7 years; 79% female). Right eyes in the treatment group were significantly more likely to show an improvement of ≥0.5-units in PLWE scores at 2 months than the no treatment group (P = 0.04), but no left eye differences were noted. Korb and PLWE scores were significantly better in the treatment group compared with the no treatment group starting at 2 weeks and remained so for the duration of the study (all P Conclusions: Perfluorohexyloctane significantly reduced LWE and improved dry eye symptoms compared with no treatment, suggesting that PFHO may enhance ocular lubrication and reduce friction-related damage. Masked, randomized, trials are still needed to compare PFHO to other treatments in participants with LWE to support generalizability of results. ClinicalTrials.gov study NCT06671041.</p

Multi-stage deep learning for intrusion detection in Industrial Internet of Things

The Industrial Internet of Things (IIoT) facilitates enhanced automation, predictive maintenance, real-time monitoring, and data analytics across various sectors, including manufacturing, energy, transportation, agriculture, and supply chain management, thereby improving productivity, efficiency, and operational safety. However, as IIoT networks continue to expand, it is imperative to secure them against increasingly sophisticated cyber threats. Deep Learning (DL) techniques have been extensively utilized for intrusion detection within IIoT systems. Nevertheless, addressing the class imbalance problem remains a significant challenge. The underrepresentation of certain attack types in training data frequently results in the development of DL models that struggle to accurately detect these categories of malicious activities. This limitation represents considerable risks to the security of IIoT networks, as undetected attacks and false alarms may lead to severe operational disruptions. In this paper, we propose a multi-stage deep learning (MSDL) method specifically designed to enhance intrusion detection within IIoT networks by addressing the class imbalance issue. We assessed the effectiveness of our approach utilizing two highly imbalanced datasets: X-IIoTID and WUSTL-IIoT. Our experimental findings indicate that the proposed MSDL method surpasses the baseline DL models as well as state-of-the-art oversampling and undersampling techniques. Specifically, the MSDL method exhibits significant improvements in recognizing minority-class attacks that are frequently misclassified. Consequently, the implementation of the MSDL for intrusion detection is anticipated to strengthen the overall security and resilience of IIoT systems, providing stronger protection against a diverse array of cyber threats in industrial applications.</p