Search Results (361)

Background/Objectives: Tacrolimus, an immunosuppressant, is increasingly used topically in ophthalmology, particularly for conditions like vernal keratoconjunctivitis and post-keratoplasty rejection prophylaxis. This systematic review aims to evaluate the efficacy and safety of topical tacrolimus in these ocular conditions. Methods: A thorough search was conducted in PubMed and Cochrane Library for relevant studies published up to 16 March 2025. Studies were eligible for inclusion if they were randomized controlled trials investigating topical tacrolimus in human ocular disease, were published in English, and reported clearly defined outcomes. Exclusion criteria included non-randomized studies, animal studies, systemic treatments, non-English publications, and studies lacking clearly reported outcomes. Data regarding study design, patient demographics, intervention details, and outcomes were extracted and analyzed. The Cochrane risk-of-bias tool (RoB 2.0) was used to assess the risk of bias. Results: A total of 10 studies met the inclusion criteria, were retrieved, and were categorized as not highly biased after the risk-of-bias assessment. These studies were included in the systematic review, where a qualitative analysis took place. Our analysis revealed that the topical use of tacrolimus showed promising results, as it improved clinical signs and symptoms in most patients. In half of the studies, tacrolimus demonstrated superior efficacy compared to the control group, while in the remaining studies, it showed equivalent efficacy. Adverse effects, such as a burning sensation, were noted in 7/10 studies but were generally mild. The methodologies were somewhat heterogeneous, and some studies had small sample sizes. Conclusions: Topical tacrolimus shows promising effects in managing various ocular surface diseases. While randomized controlled trials provide evidence, further research with larger sample sizes is necessary to solidify its efficacy and safety profile compared to other immunosuppressants. Full article

In modern medical education, a robust understanding of statistics is essential for fostering critical thinking, informed clinical decision-making, and effective communication. This paper explores the synergistic value of early and continued statistical education for medical students and residents, particularly in relation to the expanding field of sports supplementation and its impact on athletic performance. Early exposure to statistical principles enhances students’ ability to interpret clinical research, avoid cognitive biases, and engage in evidence-based practice. Continued statistical learning throughout residency further refines these competencies, enabling more sophisticated analysis and application of emerging data. The paper also addresses key challenges in integrating statistics into medical curricula—such as limited curricular space, student disengagement, and resource constraints—and proposes solutions including interactive learning, case-based teaching, and the use of public datasets. A unique emphasis is placed on connecting statistical literacy to the interpretation of research in sports science, particularly regarding the efficacy, safety, and ethical considerations of sports supplements. By linking statistical education to a dynamic and relatable domain like sports performance, educators can not only enrich learning outcomes but also foster lasting interest and competence in quantitative reasoning. This integrated approach holds promise for producing more analytically proficient and clinically capable physicians. Full article

Stable isotope analysis has become a key tool in paleontology, providing insights into ancient diets, ecosystems, climates, and environmental shifts. Despite the growing importance of isotopic studies in South America, no comprehensive bibliometric review has been conducted until now. This study addresses that gap, analyzing the development of the field over the past thirty years. Our results show a rapidly expanding discipline, especially in the last five years, with increasing publication rates and participation from South American researchers, particularly in Brazil and Argentina. However, the analysis also reveals persistent biases: notably, a strong focus on the Quaternary period, which limits broader evolutionary interpretations. Keyword co-occurrence points to dominant themes such as paleodiet, paleoecology, and megafaunal extinction, while highlighting new trends like ecological niche modeling and nitrogen isotope applications. The co-authorship network reflects high levels of collaboration, particularly with Spain and the United States. A marked gender imbalance in authorship is also evident, calling attention to the need for greater inclusivity. This review emphasizes the importance of addressing taxonomic and temporal gaps, strengthening interdisciplinary and international networks, and promoting equity in order to ensure the continued growth and global relevance of isotopic paleontology in South America. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD. Full article

Microcystis is a genus of cyanobacteria responsible for harmful algal blooms (HABs) in freshwater ecosystems, posing significant ecological and public health risks. Despite its importance, current genomic resources are heavily biased toward Microcystis aeruginosa, limiting comprehensive understanding of genomic diversity within the genus. In this study, we present the first complete genome sequences of two morphospecies, M. ichthyoblabe FBCC-A1114 and M. protocystis FBCC-A270. Using long-read sequencing, both genomes were assembled into single circular chromosomes of 5.84 Mb and 5.76 Mb, respectively. Phylogenetic analyses placed both strains within genospecies G, alongside M. aeruginosa and M. viridis. Comparative analysis of biosynthetic gene clusters revealed that, while most genospecies G members harbor aeruginosin, cyanobactin, and microviridin gene clusters, the two newly sequenced strains lack cyanobactin and microcystin clusters but retain the microginin cluster. Synteny analysis demonstrated high structural conservation between the two genomes, while notable structural variations were observed when compared with M. aeruginosa NIES-298. These findings reveal both functional and structural plasticity within the genospecies, suggesting ecotype diversification driven by environmental adaptation. The newly assembled genomes provide critical resources to refine classification frameworks and advance our understanding of Microcystis genomic diversity. Full article

Background/Objectives: Artificial intelligence (AI) holds great potential to reshape the academic paradigm. They can process large volumes of information, assist in academic literature reviews, and augment the overall quality of scientific discourse. This narrative review examines the application of various AI tools in surgical research, its present capabilities, future directions, and potential challenges. Methods: A search was performed by two independent authors for relevant studies on PubMed, Cochrane Library, Web of Science, and EMBASE databases from January 1901 until March 2025. Studies were included if they were written in English and discussed the use of AI tools in surgical research. They were excluded if they were not in English and discussed the use of AI tools in medical research. Results: Forty-two articles were included in this review. The findings underscore a range of AI tools such as writing enhancers, LLMs, search engine optimizers, image interpreters and generators, content organization and search systems, and audio analysis tools, along with their influence on medical research. Despite the multitude of benefits presented by AI tools, risks such as data security, inherent biases, accuracy, and ethical dilemmas are of concern and warrant attention. Conclusions: AI could offer significant contributions to medical research in the form of superior data analysis, predictive abilities, personalized treatment strategies, enhanced diagnostic accuracy, amplified research, educational, and publication processes. However, to unlock the full potential of AI in surgical research, we must institute robust frameworks and ethical guidelines. Full article

Promoting urban energy transition is essential for achieving environmental sustainability, yet how to effectively guide this process through public policy remains a key research question. This study aims to evaluate the effectiveness of government policy in facilitating urban energy transition, with a specific focus on China’s National New Energy Demonstration City Construction (NEDC) Policy. Using a difference-in-differences model with panel data from 274 Chinese cities, the empirical results indicate that the NEDC policy significantly advances urban energy transition, resulting in a notable increase of 0.571 units in the Urban Energy Transition Index and an improvement of 0.0321 units in the Urban Energy Transition Efficiency Index. Mechanism analysis further reveals that the NEDC policy promotes urban energy transition primarily by advancing financial development, strengthening environmental regulations, and encouraging capital-biased technological progress. Heterogeneity analysis indicates that the NEDC policy significantly boosts urban energy transition in resource-based cities, whereas it exerts a suppressive effect on urban energy transition in non-resource-based cities. This study offers valuable policy implications for developing countries seeking sustainable urban transformation. Full article

Currently, there are no validated instruments in Italian specifically designed to assess mental illness stigma or prejudice. Moreover, implicit measures, while insightful, are often resource-intensive and impractical for large-scale population studies of Italian speakers. The present study investigated the validity of the Italian version of the Prejudice towards People with Mental Illness scale (PPMI-IT) in measuring biases toward individuals with mental health issues. The original instrument by Kenny et al. was translated from English into Italian and vice versa. A sample of 455 Italian-speaking participants (65% female; M_age = 33.39; SD = 13.21) was utilized to conduct a confirmatory factor analysis, confirming a four-factor structure (fear/avoidance, malevolence, authoritarianism, unpredictability). Factor loadings indicated that each dimension was well represented, supporting the construct validity of the scale. Model fit indices, including chi-square (χ² = 782.54, df = 296.00, χ²/df = 2.64), RMSEA (0.06, 90% CI: 0.060–0.07), CFI (0.93), TLI (0.91), and SRMR (0.06), suggest an excellent model fit. Furthermore, the analysis of correlations and the heterotrait/monotrait (HTMT) ratio provides evidence supporting the discriminant validity of the PPMI scale compared with social desirability. These findings confirm that the PPMI scale is a valid and reliable tool for assessing biases toward individuals with mental health issues, making it suitable for academic research, clinical interventions, and public policy contexts. Full article

Background: The early detection of type 1 diabetes (T1D) through screening for major islet autoantibodies is receiving increasing attention as a public health strategy, exemplified by the recent implementation of a pilot pediatric screening program in Italy. The transition from research-based screening to large-scale population initiatives needs automated and standardized assays that are capable of processing extensive sample volumes. Hence, this study aimed to evaluate the analytical performance and comparability of a fully automated chemiluminescence immunoassay (CLIA) compared to a conventional enzyme-linked immunosorbent assay (ELISA) for the detection of three classes of major islet antibodies—anti-GAD (GADA), anti-IA-2 (IA-2A), and anti-ZnT8 (ZnT8A). Methods: A total of 104 serum specimens were analyzed for each autoantibody using both ELISA (RSR and Medyzim, DYNES, DSX) and CLIA (MAGLUMI 800). Assay precision and linearity were assessed through intra-assay variability studies and dilution protocols. Methods agreement was evaluated with Passing–Bablok regression, Spearman’s correlation, Bland–Altman analysis, and Cohen’s kappa statistics. Results: The CLIA showed good precision and excellent linearity across clinically relevant concentration ranges of all islet antibodies. Correlation coefficients and categorical agreement between CLIA and ELISA were high (r > 0.96 and Cohen’s kappa >0.8 for all), with ZnT8A exhibiting the highest concordance. However, proportional biases were found, as CLIA systematically underestimated GADA and ZnT8A levels, while overestimated IA-2A compared to the ELISA. Conclusions: The CLIA displayed satisfactory precision and agreement with ELISA for GADA, IA-2A, and ZnT8A detection. Our findings support the use of these automated immunoassays in large-scale population initiatives for diagnosing T1D, but we also highlight the need for further efforts to achieve better inter-assay harmonization. Full article

Family structure has long been regarded as an important determinant of household saving, yet the empirical evidence for developing economies remains limited. Using the 2018–2022 panels of the China Family Panel Studies (CFPS), a nationwide survey that follows 16,519 households across three waves, the present study investigates how family size, the elderly share, and the child share jointly shape saving behavior. A household fixed effects framework is employed to control for time-invariant heterogeneity, followed by a sequential endogeneity strategy: external-shock instruments are tested and rejected, lagged two-stage least squares implement internal instruments, and a dynamic System-GMM model is estimated to capture saving persistence. Robustness checks include province-by-year fixed effects, inverse probability weighting for attrition, balanced-panel replication, alternative variable definitions, lag structures, and sample filters. Family size raises the saving rate by 4.6 percentage points in the preferred dynamic specification (p < 0.01). The elderly ratio remains insignificant throughout, whereas the child ratio exerts a negative but model-sensitive association. A three-path mediation analysis indicates that approximately 26 percent of the total family size effect operates through scale economy savings on quasi-fixed expenses, 19 percent is offset by resource dilution pressure, and less than 1 percent flows through a precautionary saving channel linked to income volatility. These findings extend the resource dilution literature by quantifying the relative strength of competing mechanisms in a middle-income context and showing that cost-sharing economies dominate child-related dilution for most households. Policy discussion highlights the importance of public childcare subsidies and targeted credit access for rural parents, whose saving capacity is the most constrained by additional children. The study also demonstrates that fixed effects estimates of family structure can be upward-biased unless dynamic saving behavior and internal instruments are considered. Full article

Background: The increased presence of metabolic syndrome (MetS) indicators globally is considered a public health problem, and the dose-response of exercise is not clarified. Objectives: This purpose of this study was to analyze the effectiveness of changes in biochemical, physiological, and anthropometric indicators of MetS based on distinct types of exercise in adults. Methods: Based on PRISMA guidelines, we searched the following databases—PubMed, Cochrane, LILACS, Springer Link, and Science Direct— for clinical trials investigating the effect of exercise in MetS indicators, without date or language restrictions. The quality of evidence and risk of biases were assessed using the PEDro scale. The impact of aerobic training “AT,” resistance training “An-T”, concurrent training “CT”) on MetS indicators (body mass index (BMI), waist circumference (WC), total body weight (TBW), total cholesterol (TC), LDL-c, HDL-c, fasting blood glucose (FBG), triglycerides (TG), and systolic and diastolic blood pressure (SBP, DBP)) were included in this analysis. Results: A total of 10 clinical trials was selected. Results demonstrate a heterogeneity of over 50% (p < 0.001). A mean difference was found for TC by AT (−23.70 mg/dL, p= 0.003) and An-T (3.91 mg/dL, p= 0.003); on HDL by CT (0.12 mg/dL, p = 0.004); on FBG by AT (−0.66 mg/dL, p = 0.02), CT (−1.42 mg/dL, p = 0.01); on DBP by AT (−0.79 mmHg, p = 0.02). Conclusions: There is a dominance of concurrent exercise over other types of exercise, considering the greater effectiveness and significance of the effect of seven MetS indicators, including HDL and fasting blood glucose, with a major effect size. Full article

Spatial transcriptomics is an emerging technology that maps gene expression within tissue architecture. Its expanding use in medicine and veterinary science supports research, precision diagnostics, biomarker discovery, and development of targeted treatment strategies. While spatial transcriptomics applications in human health are well-documented with significant publication diversity and volume, published applications in veterinary medicine remain limited. A comprehensive search of PubMed was conducted, focusing on studies published from 2016 to early 2025 that employed spatial transcriptomics in the context of disease research, diagnosis, or treatment in human or animal health. The review followed the Arksey and O’Malley framework and adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 1398 studies met the inclusion criteria. The studies highlighted emerging trends of comparative research with animal model use for human health research. Commonly used spatial transcriptomics platforms included 10× Visium, Slide-seq, Nanostring (GeoMx, CosMX), and multiplexed error-robust fluorescence in situ hybridization (MERFISH). Key gaps in publications include limited veterinary representation, interspecies comparisons, standardized methods, public data use, and therapeutic studies, alongside biases in disease, species, organ, and geography. This review presents the current landscape of spatial transcriptomics publications for human and animal research and medicine, providing comprehensive data and highlighting underrepresented research areas and gaps for future consideration. Full article

Health disparities among marginalized populations persist in many developed countries despite substantial population health advancements, highlighting persistent systemic inequities. Visible minorities, defined as the non-White and non-Indigenous racialized population in Canada, face earlier disease onset, worse outcomes, barriers to care, and shorter life expectancy. Conventional single-axis research frameworks, which examine factors like race, gender, or socioeconomic status in isolation, often fail to capture the complex realities of these disparities. Intersectionality theory, rooted in Black feminist thought and Critical Race Theory, offers a crucial lens for understanding how multiple systems of oppression intersect to shape health outcomes. However, its application in health research remains inconsistent, with often inadequate and tokenistic applications of this theory attributable to the limitations of a research approaches and resources, as well as biases from researchers. Integrating intersectionality with other relevant frameworks and theories in population health, such as ecosocial theory that explains how social inequalities become biologically embodied to create health inequities, strengthens the capacity to analyze health inequities comprehensively. This article advocates for thoughtful application of intersectionality in research to understand health disparities among visible minorities, urging methodological rigor, contextual awareness, and a focus on actionable interventions. By critically embedding intersectional principles into study design, researchers can move beyond describing disparities to identifying meaningful, equity-driven solutions. This approach supports a deeper, more accurate understanding of health inequities and fosters pathways toward transformative change in public health systems. Full article

Life-cycle cost assessment has gained increasing relevance across sectors related to urban and building development. In real estate and public procurement decision-making, it offers a comprehensive view of property costs beyond the initial investment, which aligns with European Sustainable Development policies and new taxonomies in sustainable investment. Life-cycle cost assessment supports sustainable design decisions by integrating multiple perspectives and methodologies, including Whole Life Costing and Net Present Value calculations. This approach enables a comprehensive evaluation of long-term costs and benefits, assessing their impact on economic viability and profitability throughout the investment life cycle. However, several challenges persist in standardizing methodologies, developing comprehensive data inventories, and ensuring consistency in result interpretation. The absence of universally accepted frameworks and guidelines introduces additional limitations for practitioners, including estimation inaccuracies, biased assessments, unreliable probability judgments, and the neglect of indirect consequences in decision-making. This review particularly emphasizes the need for interdisciplinary research to advance the integration of costs and benefits of externalities and intangibles associated with social and environmental criteria. Full article