A critical challenge in personalized medicine is identifying the optimal drug treatment for individual patients based on their unique biological profiles. Recent advancements have produced a surge in drug prioritization algorithms using patient omics data, yet few undergo rigorous in vivo testing. There is a need for an efficient, objective framework to determine the smallest experimental cohort capable of producing statistically robust results. OptiRanker, which offers a statistical simulation framework that perturbs algorithmic predictions with controlled noise and evaluates their ability to preserve true performance ranking using minimal cohorts, aims to address that issue. OptiRanker demonstrated that through weighted mean squared error (WMSE) and Spearman correlation against baseline rankings, accurate predictor rankings can be recovered across the simulated conditions tested while substantially reducing experimental scale. In in silico validation using a fixed evaluation set of 36 drug IC50s and 798 cell-line models and 3 published algorithms, the full predictor ranking was recovered with as few as six individuals and one drug for the dataset used. Overall, OptiRanker offers a reproducible, exploratory approach to optimize in vivo validation trials of drug prioritization algorithms. By focusing on the smallest statistically robust experimental designs, it addresses a key bottleneck in translating computational models into clinically actionable tools. The Python code is available at: https://github.com/OhadLandau/OptiRanker.
Oceanic environments represent a unique source of biologically active substances, which has a large spectrum of chemical composition, and has significant therapeutic potential. Marine-derived bioactive compounds have emerged as accuracy regulators of molecular signals and offer new options to modern therapeutics design. Unlike normal small molecules, these compounds are often highly specific in terms of interaction with challenging targets, including proteases, chaperones, protein-protein interaction crossroads, transcription factors, and epigenetic regulators. It is becoming clear that they have the ability to shape disease specific gene expression and cellular phenotypes through chromatin architecture, altering complex signaling networks, and immunological checkpoints. Such system level regulation is particularly relevant in multifactorial diseases including neurological diseases, cancer, and chronic inflammatory states. The advances in structural biology, computational drug design and the omics technologies have minimized reliance on direct marine harvesting and accelerated the discovery and optimization of marine biological agents. Moreover, the concept of marine-inspired scaffolds is integrated into the framework of precision medicine more often, making it possible to develop a personalized therapeutical strategy that is less risky and more efficient. This review highlights recent mechanistic findings, targets, and translational advancements of marine-derived bioactives with a focus on the importance of these advancements in the development of next-generation therapeutics based on their revolutionary nature.
Reliable antemortem diagnosis of rabbit haemorrhagic disease virus (RHDV) infection remains challenging, as confirmatory diagnosis still frequently relies on postmortem tissues. This observational proof-of-concept study evaluated a minimally invasive diagnostic workflow for in vivo hepatic sampling in rabbits clinically suspected of rabbit haemorrhagic disease (RHD). The approach integrates ultrasound-guided fine-needle aspiration cytology (FNAC), needle rinse cell block (NRCB) processing into paraffin-embedded cell tube blocks (CTBs), and immunohistochemical (IHC) detection of viral antigens. The procedure was performed under short inhalational anaesthesia with clinical monitoring. CTB sections displayed excellent cytological preservation and hepatic architecture, comparable to conventional postmortem liver sections. IHC revealed distinct and widespread cytoplasmic immunolabelling for RHDV antigens within CTBs, closely mirroring postmortem findings in all infected animals. No procedure-related complications were observed in control rabbits. These findings support FNAC-derived CTB/IHC as a feasible antemortem diagnostic approach for RHDV infection. Beyond its direct veterinary relevance, this approach may have One Health relevance within the framework of comparative hepatology and translational diagnostic method development, as naturally occurring animal diseases can provide useful models for evaluating diagnostic strategies in severe acute hepatopathies across species.
Retinal detachment (RD) is a vision-threatening condition that requires prompt intervention to preserve sight. A critical factor in treatment urgency and visual prognosis is macular involvement-whether the macula is intact or detached. Point-of-care ultrasound (POCUS) is a fast, non-invasive and cost-effective imaging tool commonly used to detect RD in various clinical settings. However, its diagnostic utility is limited by the need for expert interpretation, especially in resource-limited environments. Deep learning has the potential to automate RD detection on ultrasound, but there are no clinically available models, and prior research has not addressed macular status-an essential distinction for surgical prioritization. Additionally, no public dataset currently supports macular-based RD classification using ultrasound video. We introduce Eye Retinal DEtachment ultraSound (ERDES), the first open-access dataset of ocular ultrasound clips labeled for (i) presence of RD and (ii) macula-detached vs. macula-intact status. ERDES enables machine learning development for RD detection. We also provide baseline benchmarks by training 40 models across eight architectures, including 3D convolutional networks and transformer-based models.
Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide. Exosomes have emerged as key mediators in CRC pathogenesis and progression. However, a systematic overview of the research landscape is lacking. This study aimed to conduct a comprehensive bibliometric analysis to investigate global publication trends and research hotspots in exosome-based CRC research from 2010 to 2024. Publications were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases. After screening, deduplication, and merging, eligible studies (English-language articles and reviews published between 2010 and 2024) were included. Bibliometric analyses were performed using VOSviewer and CiteSpace. Clinical trials were additionally retrieved from PubMed to assess translational progress. A total of 3,018 publications were included. Annual publication output showed a steady increase, with a marked acceleration after 2016 (linear trend R² = 0.8607). Research originated from 100 countries. China contributed the most publications (933, 49.5%), while the United States ranks among the top in average citations per article (79.8). The most prolific authors published at least 13 articles. High-frequency keywords included "exosome miRNA", "liquid biopsy", and "Fusobacterium nucleatum". Citation burst analysis identified "drug delivery" and "immunotherapy" as emerging frontiers. Only six clinical studies were identified, all at early phases, with no exosome-based therapeutic having entered late-phase development. Exosome research in CRC has evolved steadily over the past 15 years, shifting from basic biology toward translational applications. Exosomes show promise as diagnostic/prognostic biomarkers, therapeutic targets, and drug delivery vehicles, but clinical translation remains at an early stage.
Mosquito-borne diseases remain an important global public health concern, and increasing insecticide resistance has strengthened interest in complementary personal protection tools. This study evaluated the short-term dermal tolerability and laboratory repellency of MosShield, a ready-to-use topical spray formulated with 6% w/w Litsea cubeba essential oil. Non-confidential formulation quality-control parameters, including appearance, homogeneity, pH, density, and apparent viscosity, were recorded to support formulation consistency before testing. Short-term dermal tolerability was assessed using a human skin-patch test in 48 healthy volunteers, with no visible skin reactions observed up to 96 h after application. Repellency assays were conducted following the World Health Organization arm-in-cage protocol against three medically important mosquito species: Aedes aegypti, Anopheles dirus, and Culex quinquefasciatus. The formulation showed species-dependent repellency, achieving median complete protection times of 225 min against Cx. quinquefasciatus and 120 min against both Ae. aegypti and An. dirus. These findings provide product-relevant laboratory evidence supporting further evaluation of the 6% L. cubeba essential oil spray as a plant-based topical mosquito repellent under semi-field and field conditions.
Breast biopsy markers are used to mark biopsied tissues in the breast or axilla for potential future surgical excision. This study evaluated the color Doppler twinkling strength of 37 markers using two different ultrasound systems to assess if newer technology provides sufficient twinkling for clinical use. Thirty-seven breast biopsy markers (36 commercially available and one polymethyl methacrylate (PMMA) research marker) were embedded in homogeneous gelatin phantoms and imaged using two ultrasound platforms (General Electric LOGIQ E9 and LOGIQ E10). Multiple linear and curvilinear transducers were evaluated. Two experienced breast radiologists independently scored twinkling strength in two separate sessions using an ordinal 0-4 scale, with scores ≥3 considered actionable twinkling. Statistical analyses included intra- and inter-observer reliability assessment, correlation analyses between twinkling scores and imaging parameters, and ordinal logistic regression modeling to evaluate system-, marker-, and transducer-level effects. Of the 37 biopsy markers evaluated, 14 exhibited measurable twinkling and were included in statistical analyses. Several markers that demonstrated weak or non-actionable twinkling on the E9 achieved consistently higher and actionable scores on the E10. Model-based analysis showed substantial system-dependent differences by transducers, with the L8-18i demonstrating the largest improvement on the E10 (odds ratio (OR) 251.11 [95% CI: 67.52-933.88]; p < 0.001). The PMMA marker demonstrated consistently high and reproducible twinkling across both systems (mean scores: E9 = 3.4, E10 = 3.6). Color Doppler twinkling by breast biopsy markers is strongly influenced by ultrasound system generation, transducer selection, and acquisition parameters.
Despite the promise of immune checkpoint blockade (ICB), only a minority of non-small-cell lung cancer (NSCLC) patients achieve long-term benefits. In this study, we present a single-cell spatial transcriptomic landscape of NSCLC, revealing a previously uncharacterized link between elevated glutathione peroxidase 8 (Gpx8) and resistance to PD-1 blockade. Through CyTOF, CODEX, and ATAC-sequencing analyses, we demonstrate that Gpx8 knockout in both immunocompetent and humanized mouse models suppress tumor growth. This suppression is accompanied by increased infiltration of antitumor T lymphocytes, reduced enrichment of pro-tumorigenic myeloid cells, and the formation of tertiary lymphoid structures (TLS). Mechanistically, Gpx8 inhibits the activity of the RNA-binding protein Celf1(CUGBP Elav-like family member 1) through disulfide bonding between cysteine 79 of Gpx8 and cysteine 177 of Celf1. This interaction stabilizes CCAAT-enhancer-binding protein β (C/EBPβ) mRNA, promotes CSF1 secretion, and drives the recruitment of myeloid-derived suppressor cells (MDSCs) into the tumor microenvironment. Notably, resistance to anti-PD-1 treatment in Gpx8-expressing NSCLCs can be overcome through enforced expression of Celf1, CSF1R blockade, or a mimic peptide designed to disrupt the Gpx8-Celf1 interaction. Furthermore, anti-PD-1 or rCSF1 treatment activates C/EBPβ and upregulates Gpx8 transcription, establishing a Gpx8-C/EBPβ-CSF1 feedback loop that contributes to immune evasion. These findings provide new insights into the role of Gpx8 in modulating the tumor microenvironment and offer a potential framework for enhancing the sensitivity of NSCLC to PD-1 blockade therapy.
This cross-sectional study examined the associations of parental emotional warmth and parental rejection/overprotection with short video addiction among 923 junior high school students in China, focusing on the parallel indirect pathways of self-management and social anxiety. Standardized instruments were used to assess parental emotional warmth, parental rejection/overprotection, self-management, social anxiety, and short video addiction. Partial least squares structural equation modeling and bias-corrected bootstrapping were used to estimate the direct and indirect associations. The results showed that parental emotional warmth was negatively associated with short video addiction, whereas parental rejection/overprotection was positively associated with it. Self-management and social anxiety each served as significant indirect pathways. Comparisons of the specific indirect associations further showed that the pathway through social anxiety was stronger than the pathway through self-management for both parental factors. Additional serial model analyses indicated consistent directional patterns, suggesting a supplementary linkage between self-management and social anxiety. 57.5% of the variance in short video addiction was explained by the principal parallel mediation model. Overall, the findings support an interpretation based on the Interaction of Person-Affect-Cognition-Execution model, in which family-related background factors are associated with adolescents' short video addiction through both execution-related regulation and affect-related vulnerability. These results highlight the relevance of emotionally warm family communication, reduced rejection/overprotection, self-management support, and social anxiety-related support in adolescent short-video use contexts.
Preterm birth is a major risk factor for childhood psychopathology, including higher rates of anxiety and avoidant personality traits persisting into adulthood. While extensive research has documented volumetric reductions in various brain structures associated with emotional processing in preterm individuals, the habenula, a key component in affective and reward processing, has not yet been studied in this context. This study examines whether adults born very preterm show alterations in the habenula volume and whether these alterations are linked to higher anxiety, depression and avoidant personality traits. This study utilized data from the Bavarian Longitudinal Study, comprising 103 preterm-born individuals (defined as very preterm (VP) < 32 weeks of gestational age and/or very low birth weight (VLBW) <1500 g) and 110 term-born (TB) controls. Habenula volumes were assessed using manual segmentation on T1-weighted MRI scans. Additionally, participants completed standardized psychological assessments measuring anxiety, depression, and avoidant personality traits (Achenbach Young Adult Self-Report (YASR) and Beck Depression Inventory ). A higher relative right habenula volume was observed at trend-level in the VP sub-sample when compared to TB controls (Bonferroni-corrected p = 0.080). VP/VLBW showed a significant positive correlation between the relative right habenula volume with intensity of neonatal treatment. Additionally, VP and VP/VLBW exhibited higher scores on YASR anxiety and avoidant personality scales when compared to TB controls, yet were not correlated with habenula volumes. Our findings indicate that very preterm birth is associated with a trend towards relatively increased right habenula volume and higher levels of anxiety and avoidant personality traits in adulthood. While habenula volume was not directly linked to these traits, its association with intensity of neonatal treatment suggests a genuine role of perinatal stress on adult habenular morphology.
Epilepsy results from an imbalance between excitation and inhibition of neurons, with 30-40% of cases being temporal lobe epilepsy (TLE). This study aimed to prioritize autophagy-related genes and explore their potential links to immune features in human hippocampal tissue of TLE. Two hippocampal transcriptomic datasets (GSE202101 and GSE11882) were analyzed to identify autophagy-related differentially expressed genes (ARDEGs). Functional enrichment, regulatory network, and immune cell infiltration analyses were performed. LASSO regression and consensus clustering were applied. Exploratory assessment of the candidate gene signature was conducted in an independent dataset (GSE256068). Immunohistochemistry (IHC) on clinical specimens assessed protein expression of candidate signature genes. An eight‑gene candidate signature was derived, and subsequent PPI network analysis revealed LARP1, EIF4G1, and TSC2 as hub genes among them. Exploratory assessment in an independent dataset partially replicated the expression patterns of PYCARD, EIF4G1, and TSC2, whereas LARP1 showed an opposite expression trend; overall model performance remained modest. Increased activated mast cells were observed in TLE samples, while higher levels of plasma cells, CD8⁺ T cells, and regulatory T cells were observed in controls. Two exploratory clusters with distinct immune correlation patterns were identified. IHC showed upregulation of ABL1, TSC2, and SPP1 protein levels in TLE tissue. This study prioritizes a set of autophagy-related candidate genes in human hippocampal tissue and provides an exploratory framework linking them to local immune features in TLE. Further validation in larger independent cohorts is warranted before any clinical interpretation.
Exercises that promote faster movement execution may help improve bradykinesia in individuals with Parkinson's disease (PD). To describe speed-based interventions employed to improve bradykinesia in individuals with PD, evaluate their effects, and identify gaps in the literature. The review followed PRISMA guidelines. Searches were conducted in EMBASE, MEDLINE, Scielo, Lilacs, PEDro, and gray literature sources up to December 2025. The Revised Cochrane risk-of-bias tool for randomized trials (RoB 2) and Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) tools were used to assess risk of bias. Eight studies involving 149 participants with PD were included: six randomized controlled trials (RCTs) and two non-randomized studies. Interventions included high-cadence cycling, aerobic interval training, power training, and high-speed power yoga. All studies were rated as having high risk of bias. The findings suggest that speed-based interventions may improve upper- and lower-limb bradykinesia in individuals with PD. This systematic review showed that speed-based interventions are effective in improving bradykinesia in both upper and lower limbs. However, these results should be interpreted with caution due to the high risk of bias presented in all included studies. Gaps were identified regarding the assessment of bradykinesia and the types of interventions applied. Further high-quality RCTs are necessary to draw definitive conclusions.
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Given the high prevalence of internet addiction, especially during crises among students, examining the factors that predict internet addiction can be influential in precise planning. This study aimed to investigate the mediating role of depression, anxiety, and stress in the relationship between social support and internet addiction. In this cross- sectional study, 250 students were chosen through a stratified sampling technique from the faculties of Kermanshah University of Medical Sciences, Iran, participated in 2023. For data collection, Internet Addiction Test, Depression, Anxiety, and Stress Scale (DASS-21), and Perceived Social Support Questionnaires were used. Trained interviewers personally visited the faculty and distributed the questionnaires. Structural equation modeling was employed for assessing the main hypothesis of the study. In this study, 48% of participants were male and 52% were female. Results indicated that social support had a significant negative effect on psychological distress (β = - 1.20, p < 0.001), which in turn had a significant positive effect on internet addiction (β = 0.68, p < 0.001). The total effect of social support on internet addiction was also significant (β = - 1.32, p = 0.005), with approximately 62% of this effect being mediated by depression, anxiety, and stress . Considering the mediating role of depression, anxiety, and stress and intricate association between social support and depression, anxiety, and stress, as well as internet addiction, it becomes imperative that relevant authorities, particularly during crisis such as the Covid-19 pandemic, prioritize individuals exhibiting such psychological disorders. It may also prove advantageous to implement strategies such as offering counseling and psychotherapy services, conducting workshops and educational programs, establishing supportive and calming environments, enhancing awareness regarding mental health, motivate students to participate in physical activities, artistic endeavors, and social interactions.
Accurately recognizing two-person interactions is essential for social behavior analysis and public safety monitoring in daily life. Although human skeleton data has been widely used in two-person interaction recognition, how to effectively capture interactive information in dyadic actions remains a core challenge for the accurate recognition of two-person interactions. Given the natural advantage of Graph Convolutional Networks in processing human skeleton data, we propose a Node and Edge Cyclic Embedding Graph Convolutional Network for two-person interaction recognition. Existing GCN-based approaches for two-person interaction recognition usually treat node and edge features as mutually independent. The rich interactive information inherent in two-person interaction has been overlooked. To address the difficulty of effectively capturing interaction information, we propose a Tri-Graph Cyclic Block. Furthermore, in order to achieve effective temporal modeling of skeleton sequence data, we employ the widely used Multi-Scale Temporal Convolution Block to process sequential data by modeling temporal dynamics at multiple scales. Finally, to highlight the key features that characterize coordinated local body movements between the two interactors, we design a Tri-Graph Attention Block that takes multi-graph node features as guidance. The proposed method achieves an accuracy of 99.4±0.2% on the SBU-Kinect dataset, and achieves recognition accuracies of 95.2±0.2%, 97.6±0.2%, 90.7±0.3%, and 90.8±0.2% under different evaluation protocols on the interaction subsets of the NTU RGB+D 60 and NTU RGB+D 120 datasets, respectively. The proposed method obtains competitive recognition accuracy among the comparison methods. The code is available at https://github.com/JLiu920/NECEGCN.
To explore oncology nurses' experiences of using expressive relational practices to support emotional processing among older adults with cancer and to develop a practice-oriented conceptual model intended to inform clinical practice and nursing education. A qualitative interpretive descriptive study was conducted in two tertiary cancer services in Saudi Arabia. Eighteen oncology nurses with direct experience in geriatric cancer care participated in semi-structured interviews. Of 41 eligible nurses approached, 18 were enrolled. Data were audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Two researchers independently coded the first five transcripts and iteratively refined a shared codebook; remaining coding and theme development were discussed in regular analytic meetings. Rigor was enhanced through member reflection, peer debriefing, and reflexive journaling. Four interconnected themes emerged: (1) creating space for what cannot be said, reflecting nurses' attunement to silence and indirect distress; (2) expression beyond clinical dialogue, including storytelling, life review, and symbolic or sensory pathways; (3) holding emotional weight, capturing emotional containment and professional vulnerability; and (4) negotiating boundaries in expressive care, shaped by cultural mediation and institutional constraints. These findings informed an integrative model conceptualizing expressive care as a cyclical process of relational attunement, facilitated expression, emotional containment, and contextual negotiation. In this culturally specific setting, expressive relational care appears to function as an integral, rather than adjunct, dimension of person-centered geriatric oncology nursing. Structured expressive communication training, culturally responsive practice guidelines, and institutional recognition of emotional labor may enhance patient dignity and nurse resilience; patient- and family-focused research is needed to test the transferability of these findings.
This study investigated surface polycyclic aromatic hydrocarbon (PAH) contamination in fire stations using 121 wipe samples collected from indoor spaces, vehicles, and personal protective equipment (PPE) surfaces in three fire stations in South Korea, and identified key exposure sources in vehicles and PPE. Fire station surfaces exhibited significantly higher PAH concentrations than commercial office controls, with median total PAH (Σ17 PAHs) levels 69 times higher in fire vehicles (p < 0.001), 26 times higher in vehicle bays and PPE cabinets (both p = 0.044), and 24 times higher in fire offices (p = 0.003). Among vehicles, fire survey vehicles showed the highest surface Σ17 PAHs contamination, particularly on floors, where concentrations were significantly higher than those on seats (p = 0.021) and ceilings (p = 0.012). PPE contamination was highest on the outer ankle areas of boots, followed by gloves and boot soles. Notably, the inner surfaces of turnout pants and face shields had PAH levels comparable to those on their respective outer surfaces (p > 0.05), suggesting possible contaminant transfer, although inward contaminant infiltration could not be excluded. High-molecular-weight PAHs were more prevalent on vehicle floors and boot surfaces, indicating significant surface-associated exposure potential. These findings support the need for improved contamination control measures, including routine decontamination of vehicles and PPE, enhanced PPE design to minimize pollutant ingress, and targeted policy interventions to safeguard firefighter health.
To determine whether an FDA-approved artificial intelligence computer-aided detection and diagnosis (AI-CAD) system assigns varying case scores based on imaging features, tumor characteristics, and breast density. This retrospective multisite study included patients undergoing biopsy after abnormal screening tomosynthesis across four U.S. states in 2021. A commercially available AI-CAD tool generated case-level scores (range 0-100). Imaging features were classified as calcifications, mass, mass with calcifications, architectural distortion/asymmetry, or nodal-only metastasis. Breast density was dichotomized as dense or non-dense. Tumor size, grade and pathology were also collected. Associations were assessed using univariable and multivariable linear regression. Breast cancer was diagnosed in 735/3899 patients. The mean age and case score for patients with breast cancer were 65.3±11.7 years and 78.1±22.9, respectively, both significantly higher than those of non-malignant cases (58.9±11.1 years and 31.8±25.9; p<0.001). Scores were highest for calcifications (91.6±15.2) and mass with calcifications (90.5±16.3), both significantly higher than mass alone (79.1±23.6; p<0.01). In univariable analysis, ductal carcinoma in-situ (DCIS) had higher scores than invasive cancers (p<0.05). However, in multivariable analysis, higher scores were associated with non-dense breasts, higher tumor grade, larger tumor size, older age, and specific imaging features, such as calcifications, whereas the association with pathologic subtype was attenuated. Our findings suggest AI-CAD scores differ by breast density, imaging presentation, and tumor characteristics. AI outputs may reflect a combination of imaging features and associated tumor characteristics, highlighting the importance of context-informed interpretation of AI outputs in clinical practice.