ZW10 (Zeste White 10) plays an essential role in maintaining chromosomal stability during cell division. However, its contribution to tumor development, particularly in colon adenocarcinoma (COAD) remains poorly understood. This study aimed to comprehensively investigate the expression pattern, epigenetic regulation, immune relevance, prognostic significance, and molecular interactions of ZW10 in colon cancer. We analyzed transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) to assess ZW10 expression across cancer types. Differential expression, survival analysis, DNA methylation profiling, immune cell correlation, and co-expression network analyses were conducted using bioinformatics and statistical approaches including TIMER, TNMplot, ULCAN, GSCA, and GEPIA2. Kaplan–Meier survival analysis was performed to evaluate the prognostic significance of ZW10 in COAD. ZW10 was significantly upregulated in COAD and showed strong associations with advanced tumor stage, nodal metastasis, and TP53 mutation (p < 0.001). Promoter methylation analysis revealed increased methylation levels in tumors compared to normal tissues (p < 0.001), suggesting potential epigenetic regulation. ZW10 expression positively correlated with various immune cell infiltrates, indicating the role of ZW10 in modulating the tumor microenvironment. High ZW10 levels were associated with reduced overall survival (HR = 1.23; p = 0.045), whereas higher ZW10 levels correlated with improved relapse-free survival (HR = 0.62; p = 1.7e-05). Gene co-expression analysis indicated that ZW10 is associated with key oncogenic pathways regulating mitosis and cell proliferation. ZW10 may contribute to colon cancer progression and represent a context-dependent biomarker and therapeutic target, though its prognostic value requires further validation.
The objective was to evaluate the overall economic burden of spinal muscular atrophy (SMA) in Singapore. A retrospective cohort study of electronic medical records and billing data was used to obtain medical costs from public healthcare consumption. Private medical costs, nonmedical costs from transportation and hired domestic helpers, and indirect costs due to caregiver productivity loss were estimated with a cross-sectional caregiver survey. The survey also examined intangible negative and positive effects on these caregivers. All costs were adjusted to 2024 Singapore dollar (SGD). A total of 61 patients were identified in the electronic medical records, and 15 caregivers were interviewed. One caregiver who took care of two patients with SMA was excluded from the analysis. The gross cost of inpatient admission is SGD 1663.81 (standard deviation [SD] = 986.03) per day with mean length of stay of 6.22 days (SD = 10.89) per year, while annual mean outpatient cost is SGD 940.31 (SD = 1071.28). In addition to public healthcare services, private services incur an additional SGD 5992.35 (SD = 7557.33) per year, with fixed costs for respiratory devices and mobility aids at SGD 5677.06 (SD = 4519.57) and SGD 12,412.70 (SD = 8307.35), respectively. Nonmedical costs from hiring a domestic helper, medical-related transport, and nonmedical transport amount to SGD 7002.22 (SD = 6516.36), SGD 387.86 (SD = 196.36), and SGD 840.95 (SD = 1067.81) per annum, respectively. Paid work productivity loss is estimated at SGD 42,932.88 per family per year. Average overall economic burden is estimated at SGD 62,004.03, SGD 10,840.13, and SGD 59,629.64 per patient per year from the societal perspective, healthcare system perspective, and patient perspective, respectively. SMA poses a significant economic burden on patients, families, and the healthcare system. This study informs policy discussions for SMA management, advocating for a comprehensive, compassionate approach and societal perspective in economic evaluations to capture all costs and benefits.
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and language, many rely on translations of English datasets, failing to capture cultural nuances. In this work, we propose Kaleidoscope, as the most comprehensive exam benchmark to date for the multilingual evaluation of vision-language models. Kaleidoscope is a large-scale, in-language multimodal benchmark designed to evaluate VLMs across diverse languages and visual inputs. Kaleidoscope covers 18 languages and 14 different subjects, amounting to a total of 20,911 multiple-choice questions. Built through an open science collaboration with a diverse group of researchers worldwide, Kaleidoscope ensures linguistic and cultural authenticity. We evaluate top-performing multilingual vision-language models and find that they perform poorly on low-resource languages and in complex multimodal scenarios. Our results highlight the need for progress on culturally inclusive multimodal evaluation frameworks.
The rapid adoption of artificial intelligence (AI) tools in the academe has raised pressing questions about their role in shaping educational practice and scientific writing. Among these tools, Scopus AI emerges as a platform that integrates large language models with the Scopus database to support scientific writing. This report opens with a critical introduction that situates Scopus AI within the broader landscape of AI-assisted writing tools. A subsequent section explores its relevance for learning, instruction, and assessment by outlining potential applications and by noting risks such as bias and reduced critical engagement. The report then examines emerging technology in practice by reviewing four empirical studies, which highlight Scopus AI’s contributions to reference accuracy, literature synthesis, and trend mapping, while also exposing gaps in depth and methodological nuance. The final section identifies significant challenges and conclusions, including over-reliance on abstracts, limited functionality, and ethical concerns related to bias and inclusivity. The report concludes that Scopus AI can enhance scholarly work, but its lasting value will be determined by researchers’ ability to use it critically and responsibly.
This work introduces the Ecology of Relational-Embodied-Emotional Systems (EREES), a systemic meta-model reconceptualizing emotions as emergent, self-organizing phenomena arising from dynamic feedback loops within ecological, relational, historical, and intersectional systems. Unlike traditional or reductionist models that treat emotion as biologically or cognitively determined, EREES draws on generalized complexity to explain how self-eco-reorganizing processes embed intergenerational oppression, including legacies of slavery and structural violence, in embodied responses. By critiquing biomedical reductionism and the technocratic gaze, the model frames emotional experience as a systemically organized process constituted through recursive interactions among embodied regulation, relational dynamics, and structured power relations. Analyses of parent-child, romantic, and hierarchical relationships show how emotional processes reproduce or reorganize inequities across interlocking systems. Emotional development and regulation are non-linear, historically mediated processes unfolding within relational ecologies shaped by attachment, hierarchy, and social stratification. By situating emotion within self-eco-reorganizing ecologies influenced by historical continuity and systemic inequality, EREES advances a theoretically integrated, systems-oriented framework for emotional health, well-being, and equity.
