Occurrence and characteristics of bisphenols, triclosan, and their conjugated metabolites in human urine.

Quality participation of physical activity matters for quality of life and subjective wellbeing in people with disabilities.

Rabbit monoclonal antibodies: Synergistic innovation and breakthrough based on B-cell development mechanism and single B-cell technology.

Built environment in early life is linked to heterogeneous trajectories of loneliness from childhood to adolescence in the ABCD study.

Plastic pollution in Leeward, Moorea and Cook islands (South Pacific): A baseline study.

Kuntai capsule for the treatment of menopausal symptoms induced by GnRH-a therapy: A systematic review and meta-analysis of randomized controlled trials.

The recent review by Yong etal. (Rev Med Virol. 2025; 35:e70070) proposes the entity of "Long Vax Syndrome" or Post-COVID-19 Vaccination Syndrome (PCVS). While this synthesis of anecdotal and early observational reports is a timely contribution, it necessitates a critical evaluation of the underlying evidence. This letter highlights substantial methodological limitations, including a reliance on data susceptible to significant ascertainment bias and the conflation of correlation with causation, given the non-negligible background prevalence of conditions like POTS and chronic fatigue. We argue that the pathophysiological narrative, while plausible, remains highly speculative due to a lack of direct mechanistic validation in human subjects. Furthermore, the proposed therapeutic strategies, borrowed from analogous disorders, are empiric and untested in this specific context, posing potential risks if adopted without evidence. The review by Yong etal. should thus serve not as a definitive guide, but as a critical catalyst for a disciplined research agenda. We outline essential next steps, including the urgent need for large, prospective controlled cohort studies to establish true incidence, deep phenotyping to identify distinct endotypes, and the development of standardised diagnostic criteria before randomised controlled trials can ethically evaluate interventions. Only through such rigorous inquiry can we ensure both compassionate care for affected individuals and the preservation of public confidence in vaccines.

Musical Talent and Surgical Skills: Does Playing an Instrument Help With Surgical Ability?

In today's dynamic and uncertain environments, effective decision-making processes are essential for navigating complex challenges. This paper proposes an innovative approach utilizing Fermatean fuzzy sets to enhance decision-making within heterogeneous group dynamics. Through a systematic mathematical framework, our method integrates expert preferences to find out the comparative weight of decision attribue, leveraging both Fermatean fuzzy sets and entropy calculations. Furthermore, we introduce a novel technique to assess the significance of individual experts' opinions, accounting for specific contextual factors. By synthesizing performance ratings, criteria weights, and expert inputs, our approach offers a comprehensive decision-making model. We introduce the concept of the proximity coefficient to address existing methodological limitations, enhancing the accuracy of decision outcomes. To validate our methodology, we apply it to a practical scenario involving warehouse location selection. Additionally, analysis of sensitivity is conducted to evaluate the robustness of our method across diverse scenarios, demonstrating its efficacy in uncertain environments. This research contributes to advancing decision-making practices in complex and uncertain contexts, offering a valuable tool for addressing real-world challenges.

Optical Coherence Tomography Predictive Biomarkers for Visual Outcomes after Macula-Off Rhegmatogenous Retinal Detachment: A Systematic Review and Meta-Analysis.

In today's dynamic and uncertain environments, effective decision-making processes are essential for navigating complex challenges. This paper proposes an innovative approach utilizing Fermatean fuzzy sets to enhance decision-making within heterogeneous group dynamics. Through a systematic mathematical framework, our method integrates expert preferences to find out the comparative weight of decision attribute, leveraging both Fermatean fuzzy sets and entropy calculations. Furthermore, we introduce a novel technique to assess the significance of individual experts' opinions, accounting for specific contextual factors. By synthesizing performance ratings, criteria weights, and expert inputs, our approach offers a comprehensive decision-making model. We introduce the concept of the proximity coefficient to address existing methodological limitations, enhancing the accuracy of decision outcomes. To validate our methodology, we apply it to a practical scenario involving warehouse location selection. Additionally, analysis of sensitivity is conducted to evaluate the robustness of our method across diverse scenarios, demonstrating its efficacy in uncertain environments. This research contributes to advancing decision-making practices in complex and uncertain contexts, offering a valuable tool for addressing real-world challenges.

Impaired functional neuronal connectivity in schizophrenia negative symptoms.

Introduction and objective Shift work, particularly night and rotating shifts, is increasingly common in sectors such as healthcare, transportation and industry, raising significant occupational health concerns. Circadian rhythm disruption compromises homeostasis, leading to hormonal, metabolic, and cardiovascular alterations. This review aimed to analyse evidence published between 2020–2025 on the association between shift work, metabolic syndrome, and cardiovascular diseases, exploring underlying pathophysiological mechanisms and discussing practical implications for occupational health. Methodology A narrative literature review was conducted through searches in Medline (via PubMed), Scielo, and Cochrane in April 2025. Articles published in Portuguese and English between 2020–2025 were included. Systematic and narrative reviews were considered as secondary sources. Earlier landmark studies were used only for conceptual framework. Evidence quality was assessed using the Strength of Recommendation Taxonomy. Results A total of 38 articles were identified, of which 10 met inclusion criteria. Evidence indicates an increased risk of Metabolic Syndrome (OR ~1.5) and Cardiovascular Diseases (RR ~1.2), with prevalence up to 33% among professional drivers. Longitudinal studies reported a dose–response relationship in exposures ≥10 years and gender differences, with higher risk among women. Main outcomes included abdominal obesity, hypertension, dyslipidemia, insulin resistance, and higher incidence of cardiovascular events. Metabolic Syndrome was defined variably, according to National Cholesterol Education Program – Adult Treatment Panel III, International Diabetes Federation, or World Health Organization criteria. In addition to international evidence, this review highlights Portuguese contributions published in the Revista Portuguesa de Saúde Ocupacional, reinforcing the national relevance of these findings. Discussion and Conclusion Shift work emerges as a modifiable risk factor for Metabolic Syndrome and Cardiovascular Disease, mediated by circadian disruption and hormonal and behavioral alterations. Despite consistent results, methodological limitations (such as cross-sectional designs, self-reported data, and heterogeneous definitions of shift schedules) restrict generalizability. Future long-term cohort studies should adopt standardized Metabolic Syndrome metrics and integrate objective circadian assessment. From an occupational health perspective, periodic cardiometabolic screening, sleep hygiene and healthy lifestyle promotion, and alignment of work schedules with individual chronotype are recommended to mitigate adverse effects, to increase work satisfaction and productivity. Keywords: Shift work, Circadian rhythm, Metabolic syndrome, Cardiovascular diseases, Occupational Health, Occupational Medicine and Occupational Nursing.

ABSTRACT Vacant courtyards (VCs) reflect cumulative outcomes of historical evolution and socioeconomic changes, yet their analysis remains hampered by data scarcity and methodological limitations. This study develops a retrospective prediction model integrating courtyard footprint extraction with township-level census data to reconstruct historical VC patterns in Beijing’s Miyun District. Results demonstrate that: (i) our model accurately predicts historical VC levels with superior data reliability and spatial generalizability; (ii) VCs increased by 104.66% to 32,615 between 2000 and 2020, reaching a 30.02% vacancy rate and displaying a ‘dense-center, sparse-wings’ spatial pattern influenced by topography and transportation networks; and (iii) VC evolution follows a ‘from early scattered distribution, to traffic corridor clustering, and ultimately to gradient diffusion toward peripheral areas’ trajectory with distinct growth phases. These findings provide a scientific foundation for targeted policy interventions, land allocation optimization, and predictive frameworks promoting sustainable urban-rural integration and effective VC reutilization.

Ride-pooling services have been growing in popularity, increasing the need for efficient and effective operations. The main goal of ride-pooling services is to maximise the number of passengers served while limiting wait and delay times. However, factors such as the timing and volume of passenger requests, pick-up and drop-off locations, available vehicle capacity, and the trajectory to fulfil multiple requests introduce high degrees of uncertainty, creating challenges for ride-pooling operators. This study aims to expand the current state-of-the-art Approximate Dynamic Programming (ADP) approach for ride-pooling services, introduce key extensions, and perform a comparative analysis with the Neural Approximate Dynamic Programming (NeurADP) approach to optimise the efficiency and effectiveness of these services. Specifically, we develop an ADP approach that incorporates three important problem specifications: (i) pick-up and drop-off deadlines, (ii) vehicle rebalancing, and (iii) allowing more than two passengers in a vehicle. We conduct a detailed numerical study with the New York City taxi-cab dataset and a dataset of taxi-cab requests collected in the city of Chicago. We also provide a sensitivity analysis on key model parameters such as wait and delay times, passenger group sizes, and vehicle capacity. Our comparative analysis highlights the strengths and limitations of both ADP and NeurADP methodologies. Network density and road directionality are found to significantly impact the performance. NeurADP is found to be more efficient in learning value function approximations for larger and more complex problem settings than the ADP approach. However, in less complex cases, ADP is shown to outperform NeurADP.

Older adults make up the largest proportion of nonusers of the internet. With the increasing digitalization of services, it is important to identify what interventions are effective at reducing digital exclusion in older adults. We aimed to identify what evidence exists on the effectiveness of interventions to address digital exclusion in older adults. This rapid review assessed the effectiveness of interventions to address digital exclusion in older adults aged 60 years or older. Searches were conducted in November 2023 across a range of databases and used supplementary search methods. Searches were limited to comparative studies published from 2018 onward in English. Data were analyzed using a narrative synthesis approach. A total of 21 studies were included that aimed to increase a range of digital literacy skills. Sample sizes ranged from 5 to 381. Intervention approaches varied considerably and were often multicomponent and undertaken in a variety of settings. There is evidence to suggest that a range of interventions can reduce physical, personal, and perceptual barriers and improve older adults' skills, knowledge, digital literacy, and perceived self-efficacy, reduce technophobia, and increase use of technology. Importantly, findings indicated improvements among a range of subpopulations, including those living in rural areas, at risk of social isolation, who are homebound, of lower socioeconomic groups, and individuals with visual impairment. To achieve improved and sustained digital inclusion in older adults, evidence suggests it may be important to ensure structural barriers, such as access to the internet and affordability of devices, are removed. However, all studies contained methodological limitations and may not be adequately powered to determine effectiveness. The evidence shows the potential benefits of interventions aimed at improving a range of digital skills and increasing technology use in older adults, which could help to address digital exclusion. The findings of this rapid review can inform the development and delivery of future interventions. However, it is important to consider the context in which the included interventions were used and the lack of certainty of the findings. This review also identified a lack of high-quality evidence, as all studies identified contained methodological limitations and may not have been adequately powered to determine effectiveness. In addition, consideration should also be given to those who do not wish to engage with the online world to ensure they are not left behind.

Self-esteem plays a crucial role in the socio-emotional development of elementary school children. However, psychoeducational interventions that integrate storytelling and role-play remain limited and underexplored. This study aims to evaluate the effectiveness of a role-play-based storytelling intervention in improving self-esteem among elementary school students. Using a quasi-experimental one-group pretest–posttest design, 30 children participated in six sessions of a structured intervention combining narrative-based reflection and character enactment. Self-esteem was measured using the adapted Rosenberg Self-Esteem Scale, which had undergone prior construct validation. Descriptive statistics showed an increase in mean scores from pretest to posttest. Normality assumptions were re-evaluated, and paired-sample statistical testing was applied appropriately. The results indicate a significant improvement in children’s self-esteem following the intervention. The discussion integrates theoretical perspectives from positive psychology and experiential learning while acknowledging methodological limitations, including the absence of a control group and potential measurement constraints. This study highlights the potential of combining storytelling and role-play as an engaging, developmentally appropriate psychoeducational method. Future research should explore controlled experimental designs and cultural validation to strengthen generalizability.

Background and Objectives: Chronic knee pain (cKP) affects approximately 25% of adults worldwide, with prevalence increasing over recent decades. While conventional treatments have clinical limitations, several types of electrical stimulation have been suggested to improve patients’ quality of life. The electrical stimulation literature contains inadequate patient-reported outcome measures (PROMs) data. Encouraging preliminary H-Wave® device PROMs results for chronic neck, shoulder, and low back pain have previously been published. This PROMs study’s goal is to similarly assess the efficacy of H-Wave® device stimulation (HWDS) in patients with differing knee disorders. Materials and Methods: This is an independent, retrospective, observational cohort study analyzing H-Wave® PROMs data, prospectively and sequentially collected over 4 years. In total, 34,192 pain management patient final surveys were screened for participants who were at least 18 years old, used H-Wave® for any knee-related disorder, reporting chronic pain from 90 to 730 days, with device treatment duration from 22 to 365 days. PROMs included effects on function, pain, sleep quality, need for medications, ability to work, and patient satisfaction; additional data includes gender, age (when injured), chronicity of pain, prior treatments, and frequency and length of device use. Results: PROMs surveys from 34,192 HWDS patients included 1143 with “all knee”, 985 “knee injury”, and 124 “knee degeneration” diagnoses. Reported improvements in function/ADL (96.51%) and work performance (84.63%) were significant (p < 0.0001), with ≥20% pain relief in 86.76% (p < 0.0001), improving 2.96 points (average 0–10 NRS). Medication use decreased (69.85%, p = 0.0008), while sleep improved (55.33%) in knee injury patients. Patient satisfaction measures exceeded 96% (p < 0.0001). Subgroup analysis suggests that longer device use and shorter pain chronicity resulted in increased (p < 0.0001) HWDS benefits. Conclusions: HWDS PROMs data analysis demonstrated similarly encouraging outcomes for cKP patients, as previously reported for several other body regions. Knee injury and degeneration subgroups had near-equivalent benefits, as observed for all knee conditions. Despite many reported methodological limitations, which limit causal inference and preclude broader recommendations, HWDS appears to potentially offer several benefits for refractory cKP patients, requiring further studies.

This literature review examines the empirical evidence of the impact of pastoral care, focusing on the areas that have been studied in terms of measurable and verifiable outcomes. By analysing 34 international studies published between 1990 and 2020, we assessed the methodological diversity and empirical depth of research in this field. The review encompasses qualitative, quantitative, and mixed-method studies alike, including small- and large-sample investigations. Our findings suggest that while the impact of pastoral counselling is empirically measurable and verifiable, significant methodological limitations persist, particularly regarding measurement precision and the identification of influencing factors. Compared to psychotherapies integrating religiosity and spirituality, pastoral care research is less extensive and often based on smaller, less differentiated samples, with limited attention to the professional competence levels of care providers. Most outcome studies have been conducted in hospital contexts involving patients with physical or mental health conditions and their relatives. Results highlight the need for further empirical work using more rigorous and diversified methodological approaches, broader research settings, and assessment tools adapted to the qualification level of pastoral care providers, in order to establish a more comprehensive and evidence-based understanding of pastoral care effectiveness.

Biomass resource mappings are essential tasks for sustainable energy planning, since it offers information on the potential supply, geographical availability of resources, plant sitting, transportation opportunities and roadmap towards long term renewable energy concepts that have policy relevance. Its relevance is increasing as countries are embracing low carbon economy’s roadmaps which demand for reliable spatial quantitative estimations of forest and waste residues–based biomass potentials. Despite the significant headway, there are still some loopholes in applying remote sensing and machine learning techniques. These limitations comprise scarcity of good quality field data for model calibration, poor integration of socio–economic drivers and difficulties in representing fine–scale spatial variability that hinder accurate estimate of yields at different spatial levels. This paper surveys recent machine learning methods for AGB estimation, discusses their methodological limitations, and proposes future research avenues toward scalable and robust forest biomass mapping. A combination of satellite observations, GIS–based layers and ground inventory data sets are included in the analysis as well as a variety of regression, tree based, kernel based, neural network, deep learning and hybrid modelling approaches over various land coverage areas. According to the previous works, evidence is gathered from the surveyed studies that ensemble and deep learning approaches can enhance prediction performance on multi–source data; GIS–machine learning integration contributes to better site selection and logistics analysis. The results also demonstrate the potential for a combined framework that exploits transfer learning approaches and digital twin methodologies to reduce prediction uncertainty, especially in low–data areas. Such information could help support rational decision–making activities for policymakers, planners and industry actors that consider the role of bioenergy in national energy security, climate change mitigation strategies, resource sustainability and long–term renewable energy planning.