Lung cancer screening (LCS) with low-dose computed tomography reduces mortality by up to 20%, yet uptake in the United States remains below 6% of eligible individuals. Factors contributing to low uptake include lack of awareness, eligibility confusion, stigma associated with smoking history, and nihilistic beliefs about outcomes. Stigma triggers shame-avoidance behaviors, nihilism undermines perceived screening benefit, and misinformation amplifies both by spreading inaccurate eligibility criteria and exaggerated harms. Social media increasingly shapes how individuals encounter health information, form risk perceptions, and make screening decisions. Because platform architectures differ in content modality, algorithmic curation, and user demographics, single-platform studies cannot reliably characterize the digital information environment or identify platform-specific intervention targets. This study aims to (1) systematically characterize the clinical accuracy, stigma prevalence, and decision-support quality of lung cancer and screening content across 7 major social media platforms; (2) quantify platform-specific patterns in stigma manifestation and nihilistic messaging; (3) test whether inaccurate or stigmatizing content is associated with disproportionate engagement relative to accurate, nonstigmatizing content; and (4) as an exploratory aim, identify digital opinion leaders who could serve as partners for evidence-based dissemination. This cross-sectional content analysis will examine publicly accessible posts from Facebook, Instagram, TikTok, YouTube, X/Twitter, Reddit, and Bluesky. Posts will be identified through predefined search terms across 2 content domains: LCS and lung cancer narratives (diagnosis, treatment, survivorship). The sampling strategy combines relevance-based sampling, targeting approximately 700-1000 unique posts after deduplication-a sample size providing 80% power for cross-platform comparisons assuming medium effect sizes. A structured codebook operationalizing constructs from diffusion of innovations theory, attribution theory of stigma, and health misinformation frameworks will assess accuracy, stigma, decision support, and equity. All posts will be dual-coded by trained coders. Interrater reliability will be assessed using Gwet's AC1. Analyses will include descriptive statistics, cross-platform comparisons using chi-square and Kruskal-Wallis tests, and negative binomial regression models testing whether accuracy and stigma characteristics predict engagement. Data collection began in October 2025 and is projected to be complete by July 2026. As of March 2026, data have been collected from 181 posts across 7 platforms. Results are expected to be published by December 2026. Findings will characterize accuracy patterns, stigma prevalence, benefit-harm framing, and engagement dynamics across platforms, informing clinical communication tools, navigator training, and digital intervention development. This protocol describes the first multiplatform, theory-informed analysis of lung cancer and LCS content on social media. The study will generate foundational evidence to inform stigma-informed communication strategies, decision support tools, and equitable dissemination approaches. The methodology provides a replicable framework for monitoring health information ecosystems across disease contexts.
Youth e-cigarette use rose sharply between 2013 and 2024 in the United States, prompting widespread prevention campaigns at national, state, and local levels. However, many campaigns encountered online opposition, sometimes leading to message distortion or campaign withdrawal. While previous studies have examined individual campaigns, little is known about how oppositional dynamics differ across social media platforms with distinct architectures. This study aimed to conduct a retrospective, cross-platform surveillance study of oppositional responses to US youth e-cigarette prevention campaigns, comparing tactics, themes, and engagement on Twitter (now X) and TikTok to inform platform-specific public health strategies. We collected Twitter (2014-2020) and TikTok (2020-2023) posts related to major US e-cigarette prevention campaigns using 15 campaign-specific hashtags and 4 verified prevention campaign handles. We included public, English-language posts from geographic regions allowed by the platforms. Machine learning classification and human coding were used to detect oppositional content, characterize narrative frames, and classify user types. Engagement was assessed using post-level metrics, including likes, shares, comments, and retweets. We analyzed message prevalence, engagement patterns, and oppositional themes. On Twitter, opposition comprised 26.8% (83,074/310,207) of campaign-related posts overall but dominated certain campaigns (eg, Still Blowing Smoke: 6052/6113, 99%). A small cluster of advocacy and commercial accounts generated 57.8% of opposition retweets. Dominant narratives included questioning the credibility of health authorities, claims that prevention advertisements backfired, vaping rights, and product promotion. In contrast, TikTok opposition constituted only 3.5% (108/3127, 95% CI 3.1%-3.9%) of posts and was characterized by humor (71/108, 65.7%), mockery (48/108, 44.4%), and ironic portrayals of vaping (30/108, 27.8%). Individual creators comprised 76.1% (153/201) of accounts sharing prevention posts, and opposition videos used the visibility-boosting hashtag #fyp significantly more than prevention posts (51.9% vs 32.2%; P<.001). Despite inconsistent hashtag use, prevention posts achieved higher average engagement than oppositional content. This novel cross-platform, multicampaign analysis of opposition responses to e-cigarette prevention campaigns revealed how opposition reflects distinct platform architectures. Twitter opposition was highly coordinated and amplified by commercial and advocacy accounts, especially during regional campaigns. TikTok opposition was decentralized and humor-driven, aligning with the platform's entertainment-oriented algorithm. The findings strengthen health communication by introducing a framework for evaluating platform-specific vulnerabilities and informing evidence-based campaign design. On Twitter, effective countermeasures may require real-time monitoring of social media discourse to support rapid responses to coordinated opposition. On TikTok, leveraging creator partnerships and remix-friendly content may help public health messages compete with entertainment-dominated discourse. Consistent hashtag use can strengthen engagement by minimizing the fragmentation of content visibility, and credible health sources should increasingly reinforce prevention narratives on both platforms. Greater platform accountability and transparency are needed to ensure that prevention content is not systematically deprioritized by algorithms relative to commercial promotion.
The overseas reception of classical literature through online platforms presents a critical lens for understanding cross-cultural dynamics in the digital age. This study investigates the overseas reception of English translations of Journey to the West by analyzing a corpus of 1,795 reviews from Amazon and Goodreads to examine temporal dynamics, cross-platform sentiment patterns, and topic modeling. The analysis covers four celebrated translators: Arthur Waley, Anthony C. Yu, Julia Lovell, and W.J.F. Jenner. Methodologically, we developed a hybrid sentiment lexicon by integrating a domain sentiment lexicon with AFINN, NRC, and VADER through weighted fusion, addressing the limited adaptability of general sentiment lexicons in translated literature analysis. LDA modeling was further applied to enable data-driven theme extraction. Key findings reveal a consistent year-on-year increase in review counts across all translations. Notably, despite an overall positive sentiment, significant cross-platform divergences emerge, reflecting the distinct evaluative mechanisms of digital platforms. Thematic analysis identifies three central reader concerns: translation quality, plot acceptance, and character portrayal, with plot acceptance exhibiting markedly higher negativity. Furthermore, translator-level analysis reveals performance variations across these themes. This study demonstrates how digital platforms reconfigure the valuation of literary translation, and pioneers a methodological framework for capturing the dynamic interplay between reader perception, media infrastructure, and textual mobility, offering new pathways for digital humanities research in translation studies.
As content creators increasingly migrate across digital platforms with distinct technical affordances, governance norms, and user cultures, understanding the mechanisms that drive successful adaptation becomes critical. Drawing on institutional theory, boundary-spanning theory, and social cognitive theory, this study examines how creator characteristics (content transferability, cross-platform social capital, and cultural adaptability) influence engagement performance through platform adaptation self-efficacy (PSE) and cross-cultural identity navigation (CN), as well as the moderating role of digital pioneer status. Survey data from 219 international creators who migrated from TikTok to China's RedNote platform reveal that content transferability and cross-platform social capital significantly enhance both PSE and CN, which in turn positively predict engagement performance; and that PSE and CN are critical pathways linking creator resources to performance. Moreover, digital pioneer status strengthens the impact of PSE on engagement. These findings demonstrate cognitive and relational pathways of adaptation, extend studies assuming universal role of cultural adaptability, and shed practical insights for creators and platforms navigating an increasingly fragmented digital ecosystem.
Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude threshold transfer and cross-site comparability. We developed and validated the first unified, anatomically guided deep-learning framework to harmonize PET-MRI quantification to PET-CT standards across multiple tracers and scanner manufacturers. The model learns CT-anchored attenuation representations using a vision transformer autoencoder, aligns MRI features to the CT space via contrastive objectives, and performs attention-guided residual correction. In paired same-day scans (N = 70; 18F-FDG, 18F-florbetaben, and 18F-florzolotau), cross-platform bias fell by >80% while preserving inter-regional biological topology. The framework generalized zero-shot to held-out tracers (18F-florbetapir and 18F-FP-CIT) without retraining. Multicenter validation (N = 420; three sites, four vendors) reduced amyloid Centiloid discrepancies from 23.6 to 4.1 (close to, though slightly above, PET-CT test-retest variability) and aligned tau SUVR thresholds. These results support more consistent cross-platform diagnostic cut-offs and reliable longitudinal monitoring when patients transition between modalities, establishing a practical route to scalable, radiation-sparing quantitative PET in therapeutic workflows.
Accurate neuron identification is necessary for reproducible connectomics. While examining historically described octopaminergic neurons of the Drosophila melanogaster optic lobe, I found that most octopaminergic (OA) neurons of interest remained searchable under their original names across commonly used resources. However, two neurons did not behave the same way. OA- AL2b1, which was linked to the alias LoVCLo3, and OA-AL2b2, which was linked to the alias MeVCMe1. This created a selective nomenclature problem. OA-AL2b1 was especially notable because it remained octopaminergic in the queried resources, yet its historical OA-based name was not consistently preserved as the searchable or visible label. OA-AL2b2 showed a different pattern; in current connectomic tools, it was labeled cholinergic, whereas in other databases, it still remained under octopaminergic groupings. Importantly, Busch et al. originally noted that OA-AL2b2 was not confirmed to be octopamine immunoreactive. An additional layer of ambiguity arose because neuPrint displayed a predicted neurotransmitter (NT) field, whereas Neuroglancer displayed consensus NT, both showing Acetylcholine as its NT. Together, these observations show how small inconsistencies in nomenclature and annotation can create major practical problems in neuron retrieval, interpretation, and cross-platform reproducibility. Although this report focuses on two neurons in Drosophila, the same problem can arise more broadly whenever historical names, database aliases, and current annotation systems are not interlinked.
The recent clinical development of botulinum neurotoxin serotype E (BoNT/E), valued for its rapid onset, introduces a novel therapeutic protein with an unknown long-term immunogenic risk profile. To generate a method-agnostic risk assessment, we applied a consensus computational immunogenicity framework, rigorously calibrated against the clinically observed 1-3% neutralizing antibody (NAb) incidence for BoNT/A. Our multi-platform strategy integrated an ensemble of four independent HLA class II epitope prediction algorithms, triplicate molecular dynamics simulations using distinct force fields, and three independent systems immunology models. This triangulated approach consistently identified BoNT/E as possessing a significantly enriched epitope landscape, with 73-83% more predicted strong HLA binders than BoNT/A. Biophysical simulations confirmed that BoNT/E-derived peptides form more stable complexes with HLA molecules, exhibiting a mean ΔΔG ∼ binding ∼ advantage of approximately -13 kcal/mol. Systems-level models projected a consensus threefold increase in the hazard for NAb development (HR = 3.03) and an accelerated risk for concomitant BoNT/A + E therapy. Control analyses confirmed the specificity of the signal to native BoNT/E epitope architecture, and Bayesian modelling quantified a >99% posterior probability that BoNT/E confers higher relative immunogenic risk. These predictions remain subject to the inherent simplifications of computational models relative to the complexity of human immune responses. Nonetheless, this convergent, cross-platform evidence establishes a robust risk hypothesis, underscoring the need for enhanced clinical immunogenicity monitoring for BoNT/E.
Pancreatic cancer remains one of the most lethal malignancies, largely due to delayed diagnosis. Although microRNA (miRNA) biomarkers show promise, many previous studies lack cross-platform validation and model interpretability, limiting clinical applicability. We developed and externally validated an interpretable diagnostic model based on a 20-miRNA signature using publicly available datasets. A total of 801 samples were included, of which 767 were used for model training and validation. The training cohort comprised GSE59856 and GSE85589 (n = 216), and independent validation cohorts included TCGA-PAAD and GTEx pancreas (n = 585), with additional serum-based validation (GSE128508; n = 30). Feature selection and model development were conducted exclusively within the training cohort. A Random Forest classifier was applied, and model interpretability was assessed using SHAP analysis. Diagnostic performance was evaluated using cross-validation and independent external validation. The model achieved a cross-validation AUC of 0.87 (95% CI 0.82-0.92), with sensitivity of 84.7% and specificity of 83.1% in the training cohort. External validation across independent RNA-seq and qRT-PCR datasets demonstrated AUC values ranging from 0.78 to 0.83. Performance remained broadly consistent across sample types and platforms. SHAP analysis identified miR-6875-5p, miR-196a-5p, and miR-1246 among the principal contributors to classification. Functional enrichment analysis suggested involvement in canonical cancer-related pathways. We developed and externally validated an interpretable 20-miRNA signature for pancreatic cancer diagnosis with consistent performance across independent cohorts. Although based on retrospective datasets, the structured validation strategy and explainable modeling framework provide a transparent foundation for future prospective evaluation.
To compare, across large language model (LLM) platforms, the quality, readability, and completeness of action-oriented instructions in diabetes self-management education texts, and to quantify the associations among these domains to inform model selection and risk mitigation. Ten LLM platforms were used to generate diabetes education texts (total n = 200), stratified by topic. Outcomes included the Global Quality Score (GQS), the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P), and EQIP-36 (Ensuring Quality Information for Patients, 36-item version). Text characteristics, including word count, sentence count, and syllable count, were recorded. Readability was assessed using the Automated Readability Index (ARI), Coleman-Liau Index (CLI), Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Gunning Fog Index (GFOG), Linsear Write (LW), and the Simple Measure of Gobbledygook (SMOG). Between-platform differences were evaluated using one-way ANOVA or the Kruskal-Wallis test, as appropriate. Associations between readability indices and GQS, PEMAT-P, and EQIP-36 were examined using correlation heat maps and exploratory stepwise multiple linear regression. Because the readability indices were highly intercorrelated, these regression analyses were considered exploratory and were used to identify candidate readability-related correlates rather than definitive independent predictors. GQS and PEMAT-P differed significantly across platforms (both p < 0.001), whereas EQIP-36 did not (p = 0.062). Text length and readability also varied by platform (most p < 0.001). After stratification by topic, PEMAT-P understandability, PEMAT-P total score, and GQS no longer differed significantly across topics (p = 0.356, p = 0.247, and p = 0.182, respectively), whereas PEMAT-P actionability (p < 0.001), EQIP-36 (p < 0.001), and several readability metrics remained significantly different. Difficulty indices were strongly intercorrelated, and FRES was inversely associated with multiple difficulty indices. Exploratory regression analyses suggested that greater reading burden tended to co-occur with lower GQS, PEMAT-P, and EQIP-36 scores. LLM-generated diabetes education texts exhibit marked cross-platform heterogeneity, and exploratory analyses suggest a potential trade-off between readability and both information quality and the completeness of action-oriented instructions. Clinical implementation should therefore combine careful platform selection, structured prompting with templates, human-AI review, and continuous quality monitoring to support safe, readable, and actionable patient education.
Normative modeling tools are FDA-approved clinical software applications that enable quantitative amyloid PET analysis in routine clinical practice, however implementation differences may yield non-interchangeable Z-scores, with implications for diagnosis and treatment eligibility for patients with cognitive impairment and/or suspected Alzheimer disease. This study evaluated the agreement of Z-scores derived from two widely used clinical software packages in a real-world cohort of patients with cognitive impairment/Alzheimer disease. Amyloid PET from 100 consecutive patients with cognitive impairment/Alzheimer disease obtained as part of standard-of-care evaluation at a single institution were retrospectively reviewed. Amyloid PET were post-processed using syngo.via MI Neurology (Siemens Healthineers) and MIMneuro (MIM Software/GE Healthcare). Regional Z-scores were obtained for the temporal, precuneus, posterior cingulate, parietal, frontal, and anterior cingulate cortices. Z-scores were compared per patient and stratified by Centiloid burden (low/intermediate/high: < 20/20-30/>30). Agreement was evaluated using Bland-Altman analysis and Deming regression. Agreement between Syngo.via and MIMneuro varied by region. When regional Z-scores were averaged into a per-patient composite measure, overall bias was near-zero, with tight limits of agreement (slope = 0.97 [95% CI 0.93-1.02], intercept = 0.11 [95% CI -0.05-0.26]), indicating minimal proportional and constant bias between platforms. Bland-Altman analysis showed small bias and narrow limits of agreement (LoA) in the low-centiloid group (mean bias -0.19, 95% LoA -0.93 to +0.55) and the greatest divergence in the intermediate group (mean bias -0.44, LoA -2.02 to +1.14). In the high-centiloid group, bias remained small (+0.16) with wider LoA (-1.92 to +2.24). Temporal cortex Deming regression demonstrated proportional and constant bias (slope = 0.70 [95% CI 0.66-0.74], intercept = 0.34 [95% CI 0.07-0.61]), indicating systematic underestimation of high Z-scores by MIM relative to Syngo.via. There was overall concordance between Syngo.Via and MIMneuro quantitative amyloid PET analysis software packages, however with significant region- and amyloid burden-dependent variability that increased in the intermediate-centiloid group. These differences may influence interpretation and determination of treatment eligibility in borderline cases, emphasizing that Z-scores from different commercial platforms should not be used interchangeably without cross-validation.
RNA-seq quantifies the abundance of transcripts within a biological sample and performs differential analysis between different conditions to reveal regulated gene signatures. Three challenges exist: (1) different analytical packages can often report different expression patterns and false-discovery-rates and P-values; (2) the effective use of these analytical packages requires substantial knowledge of programming and bioinformatics; and (3) there are a lack of intuitive methods to prioritize target genes for further investigation. To address these challenges, we developed Confidence, a web-based application to perform simultaneous statistical analysis of RNA-seq count data. Confidence incorporates the Confidence Score (CS), ranging from 1 to 4 to aid in gene prioritization, where 1 represents low confidence and 4 represents high confidence. The Confidence web-based application was designed for rapid and intuitive analysis of standard experimental metadata and gene count inputs providing a web-based, 'wide-net' approach to RNA-seq analysis. Gene scoring allows for unbiased gene selection and identification of novel genes strongly associated with disease/treatment models across multiple species. Pathway analysis has been integrated so that highly confident genes can be placed into biological context. Confidence provides a new strategy for target prioritization in RNA-seq analysis and the generation of publication-quality figures, which we demonstrate here using a published database.
To evaluate the quality, reliability, and user engagement of endometriosis-related videos on TikTok and Bilibili, identifying variations by platform, uploader type, and content category to inform digital health strategies. The top 100 videos per platform were retrieved using the Chinese keyword for "endometriosis." After excluding irrelevant or promotional content, 195 videos (99 TikTok, 96 Bilibili) were analyzed. Categorization included uploader type (professional individuals, nonprofessionals, institutions) and content (disease knowledge, treatment, Traditional Chinese Medicine, other). Quality was assessed via Global Quality Score (GQS), modified DISCERN (mDISCERN), JAMA benchmarks, and Video Information and Quality Index (VIQI). Engagement (likes, collections, comments, shares) and duration were recorded. Analyses used the Wilcoxon rank-sum, Kruskal-Wallis, Fisher's exact, and Spearman correlations. Professionals uploaded 83.6% of videos; disease knowledge dominated (64.1%). Bilibili videos were longer (median 281.5 vs. 64.67 s; P < .0001) with higher GQS (3.29 vs. 3.04; P = .0123), mDISCERN (3 vs. 2; P < .0001), and JAMA (1 vs. 0; P < .0001). TikTok excelled in engagement (e.g., likes 355 vs. 18.5; P < .0001). Professional sources scored higher (P < .001-.003). Treatment content was most engaging but shorter (P < .001). Engagement correlated internally (P > .7) but weakly with quality (P < .3). Videos show moderate quality, with Bilibili emphasizing reliability and TikTok virality. Professional content is superior, but the popularity-quality disconnect highlights needs for verification and education to reduce misinformation.
Accurate subtyping of acute leukemia is essential for guiding therapy and predicting patient outcomes. Morphological assessment remains challenging for distinguishing subtypes with subtle cytomorphologic differences, particularly in rare or atypical forms where reliable classification is limited. Recent computational models have attempted to automate this process. However, their clinical applicability was limited by insufficient generalizability and granularity across subtypes of acute leukemia. Here we developed a deep learning framework for automated cell-level classification and case-level subtyping of acute leukemia from Wright-Giemsa-stained bone marrow smears. The model was trained on 180,928 expert-annotated single-cell images representing 19 hematopoietic and leukemic cell categories collected from three different imaging platforms to enhance generalizability. ALSNet incorporates a dual-branch convolutional architecture and a Transformer encoder to capture both fine-grained local features and global morphological context. Internally, ALSNet achieved per-class accuracies up to 0.99 for mature cells and > 0.80 for diagnostically relevant precursors, while in an external validation from an independent platform, case-level accuracy reached 0.75 with leukemic cell percentage strongly correlated to manual review (R2 = 0.66). These results indicate that ALSNet enables robust, platform-independent morphological classification and may facilitate the early, reliable diagnosis of acute leukemia in clinical practice.
Spatial transcriptomics has transformed our ability to study tissue architecture at molecular resolution, yet analyzing these data demands navigating dozens of computational methods across incompatible Python and R ecosystems-forcing researchers to devote more effort to making tools function than to pursuing biological questions. We present ChatSpatial, a platform in which the LLM selects from pre-validated tool schemas rather than generating free-form code, with domain expertise embedded in schema descriptions for context-aware parameter inference. Built on the Model Context Protocol (MCP), ChatSpatial unifies 60+ methods across 15 analytical categories into a single conversational workflow spanning Python and R ecosystems. Replication of two published studies-recovering subclonal heterogeneity in ovarian cancer and tumor microenvironment organization in oral squamous cell carcinoma-and validation across seven LLM platforms demonstrate that schema-enforced orchestration yields near-deterministic reproducibility at the workflow level for multi-step spatial analyses. Beyond replication, exploratory cross-method analyses illustrate practical triangulation across independent analytical frameworks.
Despite the universal calibration of commercial IGF1 immunoassays to WHO IS 02/254, substantial inter-assay variability persists, leading to inconsistent patient classification. Harmonization towards a higher-order analytical anchor may reduce such variability. Four matrix-matched, multi-level serum reference materials (RMs) were prepared from donor serum and value-assigned using an LC-MS/MS method calibrated to WHO IS 02/254. Commutability was assessed according to IFCC recommendations across four immunoassays (Cobas, iSYS, Immulite, Liaison). Deming regression-based recalibration equations derived from commutable RMs were applied to patient samples and healthy donor samples. The primary quantitative endpoint was reduction in standard error of estimate (SEE) relative to the LC-MS/MS method. Age- and sex-specific LC-MS/MS-anchored reference intervals were constructed as a downstream application. Prior to recalibration, immunoassays showed positive bias relative to LC-MS/MS of up to 60%. All four RMs were commutable for Liaison and iSYS, whereas the lowest concentration RM was classified as non-commutable for Cobas and Immulite. Recalibration towards the LC-MS/MS anchor resulted in marked alignment towards the identity line and reduced pooled SEE from 7.82 to 4.89 nmol/L (-37.4%) in patient samples and from 7.34 to 2.09 nmol/L (-71.5%) in healthy samples. Although harmonization effects were assay-dependent at the individual platform level, overall cross-platform dispersion was substantially attenuated. Matrix-matched, commutable serum RMs value-assigned by a higher-order LC-MS/MS procedure enable substantial reduction of inter-assay bias and variability among IGF1 immunoassays. Harmonization towards a higher-order analytical anchor is achievable in routine practice and provides a robust foundation for consistent cross-platform interpretation.
This review critically evaluates proteomics research applied to clinical nutrition and metabolism published between mid-2024 and early-2026, examining whether recent advances have moved the field closer to clinically actionable precision nutrition applications. Large prospective cohort studies show that circulating proteomic signatures reflect dietary patterns and are associated with incident cardiometabolic, hepatic, and neurodegenerative outcomes. In most analyses, these signatures capture plausible biological pathways, but their incremental predictive value beyond established risk models appears modest. Interventional studies confirm that circulating proteins respond to dietary modification, but these trials are considerably smaller than epidemiological cohorts and proteomic-guided randomized allocation has rarely been implemented to date. Although multiomics integration and machine-learning approaches have expanded discovery and improved pathway modeling, independent validation, cross-platform consistency, and clinically meaningful risk reclassification remain inconsistently demonstrated across studies. Diet-proteome associations are biologically coherent and reproducible at the population level. Nevertheless, translation into individualized dietary prescription remains to be demonstrated at scale. Robust evidence of cross-platform consistency, formal clinical utility, and outcome-driven trials incorporating proteomic-guided interventions will be key to enabling circulating proteomics to support routine precision nutrition practice.
Fluorescence lifetime imaging (FLIm) offers label-free contrast based on intrinsic tissue properties, making it a promising tool for clinical diagnostics and intraoperative guidance. However, the lack of robust, reproducible standards for system validation limits cross-platform comparability, impedes quality assurance, and hinders clinical translation. We aim to develop and characterize a set of stable solid-state fluorescence lifetime (FLT) standards using dyed epoxy resins, with the goal of enabling reliable calibration, benchmarking, and validation of FLIm systems in both research and clinical environments. A series of solid standards incorporating different dyes were fabricated to span a range of lifetimes from sub-nanosecond to over 3.5 ns. These materials were evaluated for FLT, emission intensity, photostability under UV exposure, and fabrication repeatability. The influence of dye concentration and microstructural uniformity was assessed using a confocal microscope. The standards were also applied to validate a chip-on-tip FLIm micro-camera designed for endoscopic imaging. The dyed epoxy standards demonstrated consistent and reproducible lifetimes, good photostability, and scalable fabrication. Confocal imaging revealed some microstructural heterogeneity, whereas bulk measurements remained robust. The standards enabled effective validation of the FLIm micro-camera, including spatial and temporal resolution assessment, and highlighted platform-dependent biases in lifetime estimation. Dyed epoxy materials show strong potential as practical, scalable tools for FLIm system calibration and quality assurance. These standards may support cross-platform validation and benchmarking of emerging FLIm technologies and could contribute to the development of future regulatory frameworks for clinical adoption.
The research aims to leverage machine learning techniques to better understand the diagnosis of myofascial pelvic pain syndrome (MPPS) and to develop useful tools for clinical practice. This study retrospectively analyzed clinical data from female patients. Between January 2021 and December 2024, 1,204 MPPS cases and 1,217 healthy women from the Pelvic Floor Rehabilitation Center of Zhengzhou University's Third Affiliated Hospital were enrolled. After screening, 1,136 MPPS patients and 1,136 healthy controls were selected. Using Python 3.9, we developed prediction models with 10 machine learning algorithms: logistic regression, support vector machine (SVM), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), categorical boosting (CatBoost), k-nearest neighbors (KNN), and backpropagation (BP). Five-fold cross-validation was used to prevent overfitting. The models' performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC) to assess each algorithm's diagnostic value for MPPS. The top four models in terms of AUC, ranked from highest to lowest, were RF, CatBoost, XGBoost, and LightGBM. The top four models in terms of accuracy, ranked from highest to lowest, were CatBoost, RF, XGBoost, and LightGBM. Moreover, the top four models in terms of area under the decision curve (AUDC), ranked from highest to lowest, were CatBoost, LightGBM, XGBoost, and RF. Furthermore, we created a web-based graphical user interface (GUI) for MPPS prediction. It can be packaged for cross-platform use, thereby streamlining diagnosis and improving accessibility for healthcare providers. In conclusion, this study compared 10 machine learning algorithms for diagnosing myofascial pelvic pain syndrome. The CatBoost model showed superior performance in terms of accuracy and clinical utility. In addition, a cross-platform web-based GUI was developed, streamlining diagnosis for healthcare providers and potentially improving patient outcomes.
Cuproptosis is a recently described copper-dependent form of regulated cell death linked to mitochondrial metabolic stress and is emerging as a biologically relevant pathway in cancer. Circulating noncoding RNAs (ncRNAs), such as microRNAs, long noncoding RNAs and circular RNAs, can be quantified in body fluids and are potential liquid biopsy markers in laboratory medicine. This review evaluates the diagnostic and prognostic utility of cuproptosis-related circulating ncRNAs across human malignancies, with an emphasis on their relevance to clinical chemistry and diagnostic laboratory medicine. We synthesize current evidence linking these circulating ncRNAs to key regulators of cuproptosis, tumor stage, treatment response, and survival outcomes. We further examine their potential roles in early detection, differential diagnosis, risk stratification, and longitudinal disease monitoring, including their value relative to conventional tumor markers. The review highlights laboratory factors affecting clinical implementation, including specimen matrix selection (serum, plasma, and extracellular vesicle-associated fractions), preanalytical variability, normalization strategies, and cross-platform analytical validation. We also discuss how multimodal diagnostic models, artificial intelligence, and data-driven analytical frameworks may improve biomarker interpretation and support standardization. Overall, translating cuproptosis-related ncRNA signatures into routine diagnostic laboratory practice will require analytically validated workflows, standardized reporting, and prospective clinical validation. This review provides a laboratory medicine-focused framework for understanding the opportunities and current limitations of cuproptosis-related circulating ncRNAs as emerging biomarkers in oncology.