Aim This review delineates the mechanistic contributions of micronutrients and heavy metals to cancer and metabolic disorders by integrating ionomics with multi-elemental analytics and pharmacoinformatics for biomarker discovery, while critically synthesizing current evidence and establishing a framework for future clinical validation. Summary Micronutrients and heavy metals are indispensable for physiological homeostasis, and their dysregulation is increasingly implicated in cancer and metabolic disorders. Ionomics has emerged as a powerful system-level approach for investigating elemental distribution, homeostasis, and interactions under physiological and pathological conditions. This review summarizes recent advances in ionomics, highlighting multi-matrix elemental profiling across whole blood, serum, plasma, urine, hair, nails, and tumor tissues. Distinct ionomic signatures consistently show elevated copper (Cu), chromium (Cr), arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb), together with reduced selenium (Se), zinc (Zn), and manganese (Mn), indicating strong associations between elemental dyshomeostasis and disease progression. Disease-specific ionomic alterations across biological matrices further identify candidate elemental signatures with potential utility for diagnosis, prognosis, and therapeutic monitoring. However, the available evidence remains largely descriptive and associative, with substantial methodological heterogeneity that limits immediate clinical implementation. Conclusion Overall, ionomics provides valuable insights into disease-associated elemental dysregulation and highlights promising candidate biomarkers for cancer and metabolic disorders. Rather than demonstrating clinical potential biomarker readiness, this review provides a comprehensive evidence synthesis that maps recurring ionomic signatures, identifies current methodological and biological knowledge gaps, and establishes a foundation for future prospective validation, standardized analytical workflows, and precision medicine applications.
Background Informal caregivers of people with epilepsy (PWE) experience a substantial burden because of the chronic nature and unpredictability of seizures. Although several caregiver burden assessment tools are available to quantify this burden, little is known about which of these tools have been specifically validated for use in epilepsy caregiving across diverse global and socioeconomic settings. Aim To map and evaluate standardised tools used to assess caregiver burden in epilepsy. Methods Following PRISMA-ScR and PCC frameworks, PubMed, Embase, and PsycINFO were searched up to March 2026 for quantitative studies published in English. Data on tool characteristics, domains, and psychometric properties were extracted and synthetically analysed. Results Sixty studies conducted across 24 countries were identified, utilising 18 distinct tools. The generic Zarit Burden Interview (ZBI; 58.3 %) and Parenting Stress Index (PSI; 21.7 %) predominated. Reliability triangulation confirmed stable internal consistency for generic tools (α = 0.80–0.94) across translations. However, formal psychometric validation and advanced structural modelling (CFA/MI) were entirely absent. Domain mapping revealed that generic scales fail to capture epilepsy-specific distress, relying on broad stigma proxies and omitting nocturnal vigilance. Conversely, three paediatric epilepsy-specific tools (ESPBS, IPES, UW-CSS/UW-CBS) showed excellent reliability (α > 0.90), with the UW-CSS/UW-CBS incorporating “Benefit Scales” to track mastery. Conclusions While generic tools like the ZBI are reliable standards, epilepsy-specific tools provide more granular and contextually relevant assessments of the caregiver’s lived reality. There is an urgent need to shift from literal translation to rigorous structural validation of tool reliability and validity, and to develop dedicated, epilepsy-specific instruments for adult populations.
Visions in Indian cities lack commitment from actors and possess low normative power. To address these concerns, we take up the task of examining vision-making with a focus on institutional settings across 31 million-plus populated cities. Ostrom's Institutional Analysis and Development (IAD) framework and ADICO grammar tool are used for analysis. We ask how rules-in-use within the vision-making settings are organized and geared towards collective action, and how they affect outcomes, i.e., visions. The analysis underscores three commonalities that hinder collective action, i.e., bureaucratic hegemony and allegiance to sectoral mandates, fragmented actor constellation and weak regulatory power of visions. Within visioning settings, facilitators serve as key agents for ensuring a level playing field. We found that when facilitators are external (i.e., consultants), stakeholder coverage is wider and documentation is exhaustive. In large metropolises and megacities, the visioning process acknowledges stakeholder consultation responsibly. Perhaps fair information exchange and informal monitoring of visions by civil society actors contribute to the politicisation of vision-making. In institutional terms, visions are closer to being a strategy and less to norm-setting. The paper argues that participatory discussions in diverse and heterogeneous settings with the scope of repeat interactions will build homophily and address concerns of structural fragmentation.
The rapid expansion of the digital world has led to the opening of various new responsibilities while at the same time, it has also rapidly led to an increase in various cybercrime activities. This research intricately explores as to why individuals indulge in hacking or other sorts of offences that are committed online, thereby focusing on their personal motivation alongside the psychological thinking. It provides an explanation as to how common motives like ideological beliefs, revenge, desire for power and authority, financial gain alongside the most common factor that is thrill-seeking links them with various traits of individuals, such as moral, disengagement, curiosity, and risk-taking behaviour. This research there by examines various types of offenders, ranging from various age groups, such as beginner hackers to various sorts of cyber-criminal networks along with the politically driven groups that depict as to how the environment online along with personal psychology leads to an influence on the behaviour of an individual.
To enable efficient planning and operation of optical networks, it is essential to evaluate the Quality of Transmission (QoT) of lightpaths before they are established. The nonlinear effects in optical fibers cause deployed lightpaths to impact each other’s QoT. Therefore, reliable QoT estimation is essential, as it enables the assessment of lightpath feasibility prior to deployment and supports efficient network planning and optimization. Existing deep learning-based QoT estimation methods typically predict the Q-factor directly, which can make learning challenging under slowly varying network conditions. In this paper, we propose a Residual Transformer for Q-factor estimation that incorporates a novel temporal-mean residual prediction formulation. Instead of directly regressing the absolute Q-factor, the proposed model predicts the deviation from the temporal-mean Q-factor within the input window, and reconstructs the final prediction by adding the estimated residual to the baseline. By exploiting the strong temporal persistence of QoT measurements, the proposed formulation simplifies the regression task, enabling the model to focus on learning residual temporal dynamics rather than the complete Q-factor trajectory. The proposed framework combines this residual prediction formulation with a single-head Transformer encoder and is evaluated on a real-world dataset collected from Microsoft’s North American optical backbone network. Experimental results demonstrate that the proposed method achieves an MSE of 0.00296 and an MAE of 0.0318, outperforming nine standard baseline models, including a multilayer Long Short-Term Memory (LSTM) network, a multilayer perceptron (MLP), Support Vector Regression (SVR), a simple neural network (NN), a Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), PatchTST, iTransformer, and TimesNet. Simulation results reveal that the proposed model consistently achieves outstanding performance across multiple channels in the dataset, with an MSE of 0.00296 and MAE of 0.0318, highlighting its suitability for advanced flexible optical networks. Additional experiments show that incorporating the proposed temporal-mean residual prediction formulation into GRU and LSTM models also substantially improves their prediction accuracy, indicating that the formulation is not limited to the Transformer backbone.
