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Exploring HIV vulnerability among transgender people in Pakistan: a qualitative investigation.
Get PDFBACKGROUND: This study explored the lived experiences of transgender people in Pakistan, focusing on stigma, discrimination, family rejection, education, and unemployment and their contribution to HIV vulnerability. METHODS: The qualitative investigation included 35 transgender individuals aged 19 to 49 years from diverse professions, recruited through purposive sampling via community-based contacts and referrals from transgender community leaders. Interviews were conducted in public venues (parks and restaurants) between 12 February and 5 March 2024, primarily in Urdu (33) and Punjabi (2). Thematic saturation was reached after the 30th interview, confirming adequacy of the sample. The study used a self-developed, open-ended, semi-structured questionnaire, informed by literature in similar cultural contexts and validated through expert review and pilot testing for clarity and cultural relevance. All participants identifying as transgender men and women were included after written consent. Participant demographics were described to contextualize the sample, and Braun and Clarke's thematic analysis approach was used to interpret qualitative results. RESULTS: Primary risk factors for HIV transmission included unprotected receptive sex, inconsistent condom use, multiple sexual partners, and substance use. Economic hardships and limited employment opportunities were key drivers of engagement in sex work. Many participants reported experiencing discrimination in healthcare settings, which discouraged timely HIV testing and treatment-seeking behavior. CONCLUSIONS: Transgender people in Pakistan are involved in high-risk behaviors because of family rejection, homelessness, social marginalization, discrimination, and limited access to education and employment opportunities
Ship emissions and fuel economy under transient conditions: Revisiting the propeller law.
Get PDFIn ports and coastal areas, ship engines frequently operate under aggressive, transient conditions such as manoeuvring, which contribute significantly to gaseous and particulate emissions. To address these dynamics, this study investigates diesel exhaust behaviour during acceleration using a six-cylinder testbed engine. A custom driving cycle was developed to emulate propeller law-based transient cycles across varying rates of rotational speed change and load conditions. Performance and emissions were assessed under diverse load-speed scenarios to evaluate the applicability of the propeller law during transient operations. The engine response lagged behind load demands: at 12.5-50 rpm/s, the engine met 92-100% of the requested load, but at rates up to 300 rpm/s, only 56% was achieved. Rapid acceleration increased brake-specific fuel consumption, reducing efficiency. Principal component analysis (PCA) identified NOₓ, CO, PM₁, and PN as strongly correlated with operating parameters, with the first two principal components explaining 71% of the variance. Slower acceleration ramps had elevated NOₓ and CO, while PM₁ and PN rose with faster rpm changes. Regression models based on the propeller law predicted fuel consumption, NOₓ, PM₁, and PN with R2 > 0.63; CO predictions were less accurate (R2 = 0.58), indicating additional influencing factors. These findings enhance understanding of transient engine behaviour and fuel economy, informing strategies to optimise ship propulsion and improve emission inventories in port environments. In line with MARPOL Annex VI, which mandates sulphur limits in marine fuels within environmental control areas (ECAs), this study employed ultra-low sulphur diesel (ULSD). While absolute emission values may differ, the observed trends are applicable to refined marine fuels such as marine gas oil (MGO) and ultra-low sulphur fuel oil (ULSFO)
MLAD: A Multi-Task Learning Framework for Anomaly Detection.
Get PDFAnomaly detection in multivariate time series is a critical task across a range of real-world domains, such as industrial automation and the internet of things. These environments are generally monitored by various types of sensors that produce complex, high-dimensional time-series data with intricate cross-sensor dependencies. While existing methods often utilize sequence modeling or graph neural networks to capture global sensor relationships, they typically treat all sensors uniformly-potentially overlooking the benefit of grouping sensors with similar temporal patterns. To this end, we propose a novel framework called Multi-task Learning Anomaly Detection (MLAD), which leverages clustering techniques to group sensors based on their temporal characteristics, and employs a multi-task learning paradigm to jointly capture both shared patterns across all sensors and specialized patterns within each cluster. MLAD consists of four key modules: (1) sensor clustering based on sensors' time series, (2) representation learning with a cluster-constrained graph neural network, (3) multi-task forecasting with shared and cluster-specific learning layers, and (4) anomaly scoring. Extensive experiments on three public datasets demonstrate that MLAD achieves superior detection performance over state-of-the-art baselines. Ablation studies further validate the effectiveness of the modules of our MLAD. This study highlights the value of incorporating sensor heterogeneity into model design, which contributes to more accurate and robust anomaly detection in sensor-based monitoring systems
Era of Generalist Conversational Artificial Intelligence to Support Public Health Communications.
Get PDFThe integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication. We highlight the evolution and current applications of AI-driven messaging services, including their ability to provide personalized, scalable, and accessible health interventions. Specifically, we discuss the integration of large language models and generative AI in mainstream messaging platforms, which potentially outperform traditional information retrieval systems in public health contexts. We report a critical examination of the advantages of generalist CAI in delivering health information, with a case of its operationalization during the COVID-19 pandemic and propose the strategic deployment of these technologies in collaboration with public health agencies. In addition, we address significant challenges and ethical considerations, such as AI biases, misinformation, privacy concerns, and the required regulatory oversight. We envision a future with leverages generalist CAI in messaging apps, proposing a multiagent approach to enhance the reliability and specificity of health communications. We hope this commentary initiates the necessary conversations and research toward building evaluation approaches, adaptive strategies, and robust legal and technical frameworks to fully realize the benefits of AI-enhanced communications in public health, aiming to ensure equitable and effective health outcomes across diverse populations
Associations among parental self-efficacy, symptom burden in children with medical complexity, and their use of health services.
Get PDFBACKGROUND: Children with medical complexity not only have physical but also psychological symptoms. The pattern of their health service use based on symptom burden and how symptom burden affects the parental self-efficacy in symptom management is not well identified. It is crucial to understand the relationships among them for developing effective strategies in enhancing parental knowledge and skills in responding to symptom management for children with complex health conditions. METHODS: This study conducted a secondary analysis of data from a randomised controlled trial. Convenience sampling was adopted to recruit 102 parents of children from four special schools and three non-government organisations in Hong Kong between March 2023 and May 2024. Regression bootstrapping methods were used to analyse the mediating effects of parental self-efficacy between parent-reported children’s symptom burden and their health service use. RESULTS: The mediation analysis showed that parent-reported symptom burden had a significant relationship with health service use (B = 2.30, p = 0.0003; 95% Confidence interval [CI]: 1.1, 3.5) and parental self-efficacy (B = − 14.7, p = 0.0025; 95% CI: −24.7, -5.3). However, the mediating role of parental self-efficacy between the parent-reported symptom burden and health service use was not statistically significant (B = 0.17; 95% CI: −0.18, 0.62). Moreover, the parent-reported children’s symptom burden had a significant correlation with the type of CMC (r = 0.36, p < 0.001) and the education level (r = 0.22, p = 0.025). Additionally, parental self-efficacy was found to be positively correlated with their financial status (r = 0.21, p = 0.03). CONCLUSION: The severity of child symptom burden was found to have a significant direct effect on parental self-efficacy and child health service utilisation, thereby contributing valuable insights to the existing literature in this field. Understanding how these factors interact can provide valuable insights to improve the supporting system and intervention development for these parents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12912-025-04084-8
Evolutionary sex bias in cognitive response to new environmental risk factor - PM2.5.
Get PDFThe association between exposure to particulates in polluted air and cognitive impairment is an emerging and significant health concern, particularly among younger populations. Although exposure to particulate matter ≤ 2.5 μm (PM2.5) is linked with a lower estimated risk for dementia compared to traditional risk factors such as APOEɛ4 gene variants, the widespread and long-term population exposure to PM2.5 pose substantial implications for public health. This review explores the sex differences in cognitive function induced by PM2.5, which are age-dependent and distinct from the sex bias observed in Alzheimer's disease. In addition to biological sex and sex hormones, we also discuss the role of epigenetic regulation as a mechanism underlying sex-specific cognitive vulnerabilities to environmental toxins, particularly PM2.5. Understanding these differences is important for developing targeted interventions and public health strategies to mitigate the cognitive impacts of PM2.5 exposure
Can I Trust You? A Critical Review of the Perceptual, Social and Cognitive Influences on Trust
Get PDFTrust is a complex psychological phenomenon that is foundational to coordinated behaviour and societal function. This narrative review critically examines the perceptual, social, and cognitive indicators of trust, drawing on findings from multiple fields of behavioural science to identify theoretical and empirical gaps in our current understanding. Particular attention is paid to social identity cues, perceptual features like eye gaze, pupil dilation, and facial characteristics, and memory recall factors related to threat and reliability judgements, to highlight the potential domains of functional overlap that are ignored by narrowly focused research approaches. The review also identifies critical gaps in understanding the interplay of multimodal sensory cues, the impact of trust violations on memory encoding and retrieval, and the integration of trust cues in ecologically valid settings. To ground the importance of the dynamics of trust formation, the paper then overviews some demonstrable effects trust has on other domains of decision-making, ranging from financial to legal. Recommendations for future research emphasise the need for interdisciplinary approaches to account for these complexities. Pursuing these empirical directions could lead to significant practical advances in the understanding of the cognitive mechanisms underpinning trust, and their application to real-world decision-making settings where trust is essential
Split Unlearning
No full textWe introduce Split Unlearning, a novel machine unlearning technology designed for Split Learning (SL), enabling the first-ever implementation of Sharded, Isolated, Sliced, and Aggregated (SISA) unlearning in SL frameworks. Particularly, the tight coupling between clients and the server in existing SL frameworks results in frequent bidirectional data flows and iterative training across all clients, violating the “Isolated” principle and making them struggle to implement SISA for independent and efficient unlearning. To address this, we propose SplitWiper with a new one-way-one-off propagation scheme, which leverages the inherently “Sharded” structure of SL and decouples neural signal propagation between clients and the server, enabling effective SISA unlearning even in scenarios with absent clients. We further design SplitWiper+ to enhance client label privacy, which integrates differential privacy and label expansion strategy to defend the privacy of client labels against the server and other potential adversaries. Experiments across diverse data distributions and tasks demonstrate that SplitWiper achieves 0% accuracy for unlearned labels, and 8% better accuracy for retained labels than non-SISA unlearning in SL. Moreover, the one-way-one-off propagation maintains constant overhead, reducing computational and communication costs by 99%. SplitWiper+ preserves 90% of label privacy when sharing masked labels with the server
Histone-based liquid biopsy discriminates between myelodysplastic syndrome and solid malignancies.
No full textBACKGROUND: Cancers can be hematological or solid, sharing many hallmarks, although their clinical behaviors are distinct. Identifying biomarkers that differentiate hematological from non-hematological malignancies could aid differential diagnosis by providing the basis for developing point-of-care diagnostic devices. In this respect, complex histone populations are secreted and detectable in biological fluids in various disease settings. To our knowledge, studies analyzing the circulating histone profile complexity by comparing healthy individuals, patients with hematological malignancies, and solid cancer patients are currently lacking. RESULTS: We assessed the plasma histone signature of healthy subjects (n = 30), and of patients with myelodysplastic syndrome (MDS, n = 43), colorectal cancer (CRC, n = 39), lung cancer (non-small cell lung cancer [NSCLC, n = 15]), small cell lung cancer [SCLC, n = 4]), or breast cancer [BC, n = 16]). Principal component analysis (PCA) demonstrated the segregation of circulating histones and histone complexes between oncological and healthy patients. Individual histones (H2A, H2B, H3, H4, macroH2A1.1, and macroH2A1.2), histone dimers and nucleosomes were assayed by ImageStream(X)-advanced flow cytometry. We found general increases in circulating histone abundance in the blood of cancer patients versus healthy controls. MDS and solid cancers could be discriminated among themselves for an increased abundance of histones H2A and macroH2A1.2 (p < 0.01), and a decreased abundance of H2A/H2B/H3/H4 and H3/H4 histone complexes (p < 0.01). Moreover, macroH2A1.2 and H2A/H2B/H3/H4 levels negatively or positively correlated with age in healthy subjects versus MDS patients, respectively. CONCLUSIONS: Overall, we identified circulating histone signatures able to discriminate between solid and MDS, using a rapid and non-invasive imaging technology, which may improve patient diagnosis
Enablers and barriers for scaling up non-communicable disease interventions across diverse global health contexts: a qualitative study using the Consolidated Framework for Implementation Research.
Get PDFOBJECTIVES: To identify enablers and barriers for scaling up non-communicable disease (NCD) interventions across diverse global contexts and to map these factors to the WHO's health system building blocks. DESIGN: A multi-method qualitative study applying the Consolidated Framework for Implementation Research to analyse data from multiple projects nearing or completing scale-up. SETTING: Global Alliance for Chronic Diseases-funded implementation research projects conducted across 18 low- and middle-income countries and high-income settings. PARTICIPANTS: Data was derived from documents (n=77) including peer-reviewed publications, policy briefs, and reports and interviews with stakeholders (n=18) (eg, principal investigators, medical professionals, public health workers). INTERVENTIONS: Various context-specific interventions targeting sustainable scale-up of NCD (eg, diabetes, hypertension, cardiovascular disease) interventions at the community, primary care or policy levels. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was identifying contextual enablers and barriers to intervention scale-up. Secondary outcomes included exploring how these factors aligned with health system building blocks (eg, leadership/governance, healthcare workforce). RESULTS: Twenty enablers (eg, intervention adaptability, strong stakeholder engagement, local empowerment) and 25 barriers (eg, resource limitations, intervention complexity, stakeholder burnout) were identified. Contextual alignment, supportive governance and capacity building were critical for sustainability, while cultural misalignment and socio-political instability frequently hampered scaling efforts. CONCLUSIONS: Tailoring interventions to local health systems, ensuring stakeholder co-ownership and incorporating strategies to mitigate stakeholder burn-out are essential to achieving sustainable, scalable NCD solutions. Future research should focus on integrating systematic cultural adaptation, sustainable financing and workforce capacity building into scale-up planning
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