Financial entity-level sentiment analysis aims to identify sentiment polarity toward specific financial entities in financial texts. This task is challenging because a single sentence may contain multiple entities with opposite sentiment orientations, while financial sentiment is often conveyed through domain-specific and implicit cues, increasing the risk of entity confusion. To address these challenges, we propose MSAR, a Multi-Stage Adversarial Representation Learning framework built on a FinBERT-CRF backbone. At the textual level, a Model-Sensitive Metric generates adversarial examples by jointly considering model sensitivity, semantic consistency, and linguistic fluency. At the representation level, Adaptive Adversarial Regularization dynamically adjusts the regularization strength according to gradient sensitivity and output-distribution variation. By integrating discrete textual perturbation with continuous representation regularization, MSAR enhances robust entity-level sentiment prediction. Experiments on FinEntity and SentFin show that MSAR outperforms representative baselines. Ablation, sensitivity, and low-resource experiments further confirm the effectiveness and robustness of the proposed components. These findings demonstrate that jointly optimizing textual and representation-level robustness improves financial entity-level sentiment analysis, particularly under low-resource conditions.

Patients with colon cancer can experience substantially different outcomes despite comparable anatomical stage, highlighting the need for molecular markers that complement conventional clinicopathological risk assessment. The interleukin-17 (IL-17) cytokine family has context-dependent roles in mucosal inflammation and tumour biology, but the prognostic relevance and cellular context of individual IL-17 receptors in colon cancer remain incompletely characterised. A prespecified family-wide analysis of 11 IL-17 ligands and receptors was performed in the GSE39582 Discovery cohort using continuous-expression Cox regression with Benjamini-Hochberg correction. The lead candidate, IL17RB, was evaluated in the predefined GSE39582 Validation subset and independently in TCGA-COAD, followed by clinical and molecular adjustment, model diagnostics, influence analyses and prognostic-model evaluation. Donor-aware single-cell analysis of GSE178341, exploratory Human Protein Atlas immunohistochemistry and DepMap Public 26Q1 colon adenocarcinoma cell-line analysis were used to characterise the cellular, protein-level and experimental-model context of IL17RB. IL17RB showed the strongest overall-survival association in the GSE39582 Discovery cohort (HR per 1-SD increase = 0.780, 95% CI 0.672-0.904; P < 0.001; q = 0.011). The association was directionally concordant in the predefined Validation subset (HR = 0.722, 95% CI 0.533-0.979; P = 0.036), although it did not remain significant after family-wide correction (q = 0.122), and was also observed for relapse-free survival. In TCGA-COAD, the simple linear association was weaker, whereas clinically adjusted spline modelling showed significant overall association and non-linearity (P = 0.003 and P = 0.005, respectively). Clinical and molecular sensitivity analyses generally retained the direction of association. Addition of IL17RB produced modest improvements in prognostic-model performance, but confidence intervals around incremental Validation metrics included zero. Donor-aware single-cell analysis localised IL17RB predominantly to epithelial cells and showed higher expression within an independently annotated tuft-cell cluster. Exploratory HPA assessment showed predominantly low or undetectable IL-17RB protein staining in colon adenocarcinoma, whereas the primary continuous DepMap analysis showed no significant association between IL17RB and the predefined tuft-cell transcriptional programme. IL17RB represents a candidate prognostic marker with complementary epithelial and tuft-cell-associated biological context. Its incremental contribution to clinical risk prediction remains modest and requires additional independent validation. Higher IL17RB expression was associated with favourable survival most consistently in GSE39582, while TCGA-COAD revealed a more complex nonlinear relationship. The combined findings support further prognostic and functional investigation of IL17RB but do not establish clinical utility, mechanistic function or therapeutic targetability.

Ferric reducing antioxidant power (FRAP) is a well-established antioxidant in vitro assay. This work introduces an innovative FRAP-analog assay based on green-synthesized carbon dots (GCDs) for the sensitive evaluation of antioxidant activity. The GCDs were prepared via ordinary roasting of sweet potato, which is considered a natural and renewable precursor, yielding highly fluorescent (quantum yield 53%), water-soluble CDs with excellent ferric (Fe[3+]) complexing activity. In this system, the addition of Fe[3+] leads to quenching of the fluorescence of GCDs. Antioxidant-induced reduction of Fe[3+] to Fe[2+] leads to a measurable turn on effect. Using ascorbic acid as a reference antioxidant, the sensor exhibited a linear response between 2 and 14 μg/mL (R[2] 0.996) with a limit of detection of 0.82 μg/mL, very good accuracy (recovery = 103.27% ± 3.48%) and acceptable precision (RSD = 1.01%-5.61%). The developed assay was applied successfully to pharmaceutical products and plant extracts. The GCD-based FRAP analog demonstrated strong correlation with traditional FRAP assays. The devised approach eliminated the use of toxic nonrenewable reagents, rendering it excellent green on both analytical Eco-scale, Analytical GREEnness (AGREE), and Nano Eco-Scale metrics. This green, nanomaterial-based approach provides a promising alternative for antioxidants' analysis in food and pharmaceuticals.

This study aimed to revisit the validated Scope of School Nursing Practice Tool (SSNPT[©]) and modify it as needed for relevance to the updated School Nursing Practice Framework, and to determine potential uses. A cross-sectional, descriptive design employing quantitative and qualitative methods was used to appraise the ongoing fidelity of the SSNPT[©] with the updated framework. Items on the SSNPT[©] achieved a scale content validity index of 99.6%. Modifications were made to the current version of the tool to address recommended language updates; no items were added or removed, thereby preserving the psychometric properties. Experts identified five potential uses for the tool: self-assessment, performance evaluation, professional development and goal setting, advocacy and stakeholder education, and program and quality improvement (benchmarking). The resulting SSNPT-R[©] is a validated tool that aligns with the updated framework and can be used to advance school nursing practice.

Understanding the structural connectivity of the human brain during fetal life is critical for uncovering the early foundations of neural function and vulnerability to developmental disorders. Diffusion-weighted MRI (dMRI) enables non-invasive mapping of white matter pathways and construction of the brain's structural connectome, but its application to the fetal brain has been limited by data scarcity and technical difficulties in analyzing fetal dMRI data. Here, we present the largest study to date of in utero brain connectivity, analyzing high-quality dMRI data from 198 fetuses between 22 and 37 gestational weeks from the Developing Human Connectome Project. We employed advanced fetal-specific tools for brain segmentation and parcellation, and used ensemble tractography to encourage more complete reconstruction of various white matter tracts. For connection weighting, we relied on the notion of fiber bundle capacity. We reconstructed individual structural connectomes and characterized the developmental trajectories. Graph-theoretical analysis revealed consistent increases in integration and segregation metrics over gestation, while bootstrapping confirmed the robustness of nodal and edge-wise developmental patterns. Furthermore, we proposed a novel method for constructing age-specific connectome templates based on aggregation of individual subject connectomes. The new method follows an optimization-based approach to ensure that the connectome templates closely represent individual subject connectomes, are temporally consistent, and proportionally preserve short and long connections. Our analysis shows that this approach is superior to spatial alignment and averaging of the data in image space, with the resulting connectome templates supporting accurate prediction of the gestational age of individual fetuses (mean error = 0.90 ± 0.81 weeks). In summary, this study provides robust normative benchmarks for fetal brain structural connectivity and demonstrates that connectome-based measures capture meaningful developmental signatures, offering a framework for future studies of early brain development.

The countermovement jump (CMJ) test serves as a popular assessment method owing to its relative simplicity, time-efficient nature and ability to elicit consistent and repeatable measures. However, the choice of metrics is vast when undertaking testing with force platforms, which makes it difficult for practitioners to know which ones to monitor and why. This article outlines some of the common challenges that practitioners face when considering the use of this test, some objective markers and subjective information to guide its implementation in practice, in addition to a blend of logic-led reasoning and referral to the existing evidence base, to consider which CMJ metrics may be most appropriate for practitioners to monitor during the rehabilitation journey of those who suffer ananterior cruciate ligament (ACL) injury.

Lumbar spinal stenosis (LSS) is a common degenerative condition that impairs mobility and quality of life, particularly in older adults. Video-sharing platforms are increasingly used for health information, yet the quality and reliability of LSS-related videos and cross-platform differences remain unclear. This study compares the content, uploader characteristics, and information quality of LSS-related videos on YouTube, TikTok, and Bilibili and examines associations between video quality and audience engagement. A cross-sectional search of YouTube, TikTok, and Bilibili was conducted on February 1, 2026, using relevant Chinese and English keywords. Of the retrieved videos, 253 met the eligibility criteria. Reliability, overall quality, understandability, actionability, and audiovisual quality were assessed using the modified DISCERN instrument, Global Quality Score, Patient Education Materials Assessment Tool understandability and Patient Education Materials Assessment Tool actionability score scores, and Video Information and Quality Index (VIQI). The sample comprised 89 YouTube, 80 Bilibili, and 84 TikTok videos, with median durations of 389, 171, and 103 seconds, respectively. TikTok videos were the shortest and showed relatively high engagement across available metrics. YouTube videos more frequently covered etiology and prevention and were predominantly uploaded by hospitals/departments and verified physicians; Bilibili and TikTok videos focused mainly on treatment, and TikTok had the highest proportion of verified physician uploaders. YouTube videos had higher modified DISCERN instrument and Global Quality Scores. Total VIQI scores were higher on YouTube and TikTok than on Bilibili, whereas Patient Education Materials Assessment Tool actionability scores did not differ significantly across platforms. Associations between video quality and audience engagement varied by platform, with VIQI showing the most consistent positive correlations. The quality and dissemination characteristics of LSS-related videos differed across platforms. YouTube videos were more reliable, TikTok videos showed greater dissemination appeal, and Bilibili showed no clear advantage in most quality metrics. High audience engagement did not necessarily indicate medical reliability. Platform recommendation systems should consider uploader qualifications, content reliability, and presentation quality to promote high-quality LSS information.