Chronic low back pain (CLBP) is a prevalent condition with unclear pathophysiology and substantial socioeconomic burden. Cerebral blood flow (CBF) alterations have been implicated in CLBP, yet previous arterial spin labeling (ASL) studies using single post-labeling delay (PLD) have yielded inconsistent results. In this study, multi-PLD ASL was combined with machine learning to characterize CBF alterations in CLBP and to explore their classification feasibility. Seventy-eight patients with CLBP and seventy-eight age- and sex-matched healthy controls underwent multi-PLD ASL scanning. Voxel-wise comparisons of normalized CBF were performed, followed by correlation analyses with clinical measures. Radiomics features extracted from brain regions showing significant CBF differences were used to construct machine learning classification models via a rigorous nested cross-validation and LASSO feature selection framework. Compared with healthy controls, patients with CLBP exhibited significant hyperperfusion in the right lingual gyrus and right thalamus. CBF values in the right lingual gyrus were positively correlated with Oswestry Disability Index scores, while thalamic CBF was positively correlated with pain intensity. Among the evaluated models, the XGBoost classifier achieved the best performance, with an area under the curve of 0.842 (95% CI: 0.774-0.901). These findings indicate that region-specific CBF alterations are closely associated with pain severity and functional impairment in CLBP. Machine learning analysis of CBF radiomic features shows potential discriminative performance in identifying patients with CLBP.
This study examines the impact of a light-cycle shift regimen on corneal and conjunctival tissues in menopausal rats and evaluates the protective role of combined hormone therapy. Twenty-four menopausal female albino rats were randomly assigned to three groups (n = 8) following a 10-day acclimatization period. Group 1 (Control+Saline) was maintained under a 12:12 light/dark cycle. Light-cycle shift regimen was induced in Groups 2 and 3 using a rotating 7-day light-exposure sequence repeated over 21 days; this protocol consisted of 24 h of continuous light, 72 h of inverted dark-light timing, and 72 h of standard light-dark conditions. Groups 1 and 2 received saline, while Group 3 received 17β-Estradiol and drospirenone daily via oral gavage. After 31 days, eyes were enucleated for histological and immunohistochemical analyses of corneal, conjunctival, and palpebral tissues, including caspase-3 (Cas-3), tumor necrosis factor-alpha (TNF-α), and PERIOD-2 (PER2) expression. Light-cycle shift regimen (Group 2) significantly increased corneal thickness (p < 0.001), conjunctival inflammation, and vascular congestion, with marked upregulation of Cas-3 and TNF-α and downregulation of PER2. Hormone therapy (Group 3) attenuated these effects, showing reduced corneal edema, diminished inflammatory infiltration, and partial normalization of molecular markers. Shifting light-dark cycles may aggravate inflammatory and apoptotic changes in the ocular surface during menopause. Estrogen-progestin therapy attenuates these alterations by modulating the expression of the circadian-associated protein PER2 and maintaining structural integrity. These findings suggest that hormone therapy may offer potential benefits for preserving ocular surface homeostasis in menopausal women experiencing sleep or circadian rhythm disturbances.
With coral reefs increasingly threatened by rapid environmental changes, understanding genetic diversity at microgeographic scale is critical for assessing their capacity to respond to local stress regimes. Theory for continuous populations predicts that brooding corals with restricted dispersal should exhibit fine-scale genetic structure and isolation-by-distance, yet such patterns remain poorly resolved in marginal and environmentally extreme reef ecosystems. Here, we investigated the genetic structure of the catch bowl coral, Isopora cf. palifera, across 11 sites within ~ 14 km in Kenting National Park (KNP), southern Taiwan, a reefscape characterized by strong small-scale environmental heterogeneity, including chronic thermal influence from a nuclear power plant and tidally driven upwelling. We genotyped 466 colonies (six microsatellite loci yielding 302 unique multilocus genotypes) and sequenced nuclear PaxC 46/47-intron from 322 colonies of I. cf. palifera. Microsatellite data revealed strong genetic structure (K = 2, K = 5): principal coordinate analyses identified four geographic groupings, and Bayesian clustering (STRUCTURE) supported two major clusters separating Nanwan (plus Tantzei Bay) from the remaining coastal sites, with one site (Shiaowan) showing admixture. The PaxC marker resolved ten haplotypes, with H1 widespread, H2 concentrated along Nanwan, and H3 dominant at thermally influenced sites near the nuclear power plant outfall. Overall, populations showed high site differentiation, significant isolation-by-distance, and high self-recruitment (68-92%), indicating limited effective dispersal. A temporal comparison (2000-2015) at Tantzei Bay indicated stable genetic structure through time despite repeated regional disturbances. Generalized estimating equation (GEE) models showed that site-level seawater temperature was positively associated with both host haplotype composition (GEE; coefficient = 0.0479, p < 0.001) and Symbiodiniaceae genera (GEE; coefficient = 0.0462, p < 0.001, symbiont data from a previous work in KNP), suggesting non-random host-symbiont-environment associations at microgeographic scale. Together, these results indicate that I. cf. palifera in KNP exhibits pronounced fine-scale genetic structure consistent with restricted dispersal and possible microgeographic adaptation of the holobiont to local thermal regimes. While such structuring may enhance local resilience by maintaining diverse, site-specific host-symbiont combinations, it also implies limited scope for rescue via gene flow if future warming pushes populations beyond their adapted tolerances. Our findings underscore the importance of accounting for microgeographic genetic structure and local adaptation when designing management and conservation strategies for reefscape such as those in KNP.
Life's Essential 8 (LE8) provides a multidimensional framework to assess cardiovascular health (CVH) in aging populations. The objective of this study was to describe LE8 component scores and their variation by age, sex, and psychosocial factors in middle-aged and older adults from Cádiz, Spain. Cross-sectional data were analyzed from 495 adults aged 50-79 years (59.4% women; 34.7% ≥ 65 years). LE8 scores were calculated following American Heart Association guidelines. Group comparisons used t-tests, ANOVA, and chi-square tests to explore differences across demographic and psychosocial variables. Age- and sex-adjusted linear regressions were fitted for CVH, health behaviors (HB), and health factors (HF). Most participants showed moderate CVH, HB, and HF scores (76.6%; 53.1%; 62.2%). Diet quality had the lowest mean (40.8 ± 31.7), while physical activity and sleep health were the highest (88.3 ± 30.6 and 85.0 ± 22.2). Middle-aged adults presented higher CVH and HF scores (mean differences [MD]: 2.5 ± 0.3; 7.8 ± 1.5), whereas older adults scored better in HB (MD: 2.8 ± 1.4). Women exhibited higher CVH, HB, and HF scores than men (MD: 3.6 ± 0.3; 2.8 ± 0.4; 4.4 ± 0.4), with middle-aged women showing the most favorable CVH profile (73.0 ± 10.5) and older men the least favorable (66.4 ± 11.0). Higher self-rated health (β = 0.240; R2 = 0.096) and educational attainment (β = 0.235; R2 = 0.090) were the strongest correlates of CVH (both P < 0.001). LE8 scoring revealed an intermediate CVH profile, with disparities by age, sex, and psychosocial context. Middle-aged women showed the most favorable profiles, while self-rated health and educational attainment emerged as key psychosocial markers for CVH assessment.
Chemokine receptor 4 (CXCR4) is a clinically significant G protein-coupled receptor implicated in HIV-1 entry, cancer progression, immune regulation, and metastatic dissemination, making it an attractive therapeutic target. This study employed an integrated computational and experimental framework to identify novel small-molecule CXCR4 inhibitors. A curated dataset of 608 compounds from peer-reviewed literature and patents was used to train machine-learning classification models. Decision Tree, Logistic Regression, and AdaBoost models showed balanced performance across key metrics, and external validation on 2146 in-house compounds identified 44 consensus CXCR4 inhibitors. Molecular docking analyses suggested favorable binding modes and key interactions comparable to those predicted for the reference inhibitor IT1t. One hundred-nanosecond molecular dynamics simulations indicated stable CXCR4-ligand complexes, with equilibration occurring within approximately 20 ns and backbone RMSD values maintained between 4 and 8 Å. MM/GBSA free-energy calculations demonstrated favorable energetics, with IS00622 exhibiting the strongest affinity (-70 kcal/mol), followed by IT1t, IS00998, and IS00179. In vitro assays identified IS00127 as a promising lead, showing strong antiproliferative activity against MDA-MB-231 cells and minimal toxicity toward HEK293 cells. ELISA assays confirmed dose-dependent CXCR4 downregulation with negligible effects on CXCR7, indicating high functional selectivity. Overall, this integrative strategy accelerates the discovery of potent, selective CXCR4 inhibitors for translational research.
. Feasible, person-centered advance care planning (ACP) approaches for persons with dementia and their care partners are needed, and the optimal approach may differ depending on the situation. Two contrasting approaches involve a highly scripted medical order-setting approach to decide on specific treatments in advance versus a flexible goal-eliciting, more psychosocially-oriented coping-based approach. . To distinguish situations in which either approach is preferred in dementia from the perspective of general practitioners. . We interviewed thirteen practitioners participating in the CONT-END program in the Netherlands. Seven were trained in the order-setting approach and six in the goal-eliciting approach for an ACP trial. Twelve other practitioners participated in a video vignette study showing the two approaches, and we triangulated findings. Inductive qualitative content analyses of interviews aimed at elucidating for whom and when an approach was preferred. . Four attributes distinguished situations in which either approach is preferred: understanding, trust, readiness and momentum. For the order-setting approach, understanding, trust, and readiness of person and care partner were prerequisites for momentum (time right to express preferences), when not triggered for urgent medical reasons. In contrast, the goal-eliciting approach would help understand the person, foster trust and create readiness from a first conversation. Without a clear trigger, however, momentum would need to be created. . Skill in employing various approaches to ACP conversations each with specific benefits could help tailor ACP to the individual and their situation. Further theoretical and empirical research including in other populations and settings may inform person-centered ACP.
Impulsivity is associated with problematic smartphone use (PSU) and social media addiction (SMA). However, it remains unclear which specific impulsivity dimension and which dimension-symptom connections were most important to these associations. In this study, network analysis was applied to examine the connections between impulsivity dimensions and the separate and comorbid symptoms of PSU and SMA. Using cross-sectional data from two independent samples of Chinese adults-a main sample (n = 1047, aged 18-26, collected in 2023) and a replication sample (n = 325, aged 18-36, collected in 2022)-three regularized partial-correlation networks were constructed for each sample: an impulsivity-PSU network, an impulsivity-SMA network, and a combined impulsivity-PSU-SMA network. Bridge centrality was calculated to identify key transdiagnostic nodes, and network comparison tests (NCTs) were performed to evaluate the consistency of findings across samples. Across all the networks, motor impulsivity consistently emerged as the most central bridge node, showing robust connections to individual symptoms of both the PSU and the SMA, whether examined separately or comorbidly. Network comparison tests further confirmed that both the bridge centrality of motor impulsivity and its specific symptom-edge weights were comparable between the main and replication samples. These findings provide novel, symptom-level insight into how impulsivity-particularly motor impulsivity-contributes to the development and comorbidity of PSU and SMA. Motor impulsivity is identified as a key transdiagnostic bridge and a promising target for early intervention. The replication of the core results across independent samples strengthens the reliability of the findings.
To understand the molecularly obscure pre-diagnostic phase of lung cancer, we mapped the temporal evolution of the plasma proteome for new biological insights and improved risk prediction. Leveraging the UK Biobank prospective cohort, we analyzed 2,921 plasma proteins from 37,759 participants, including 342 incident lung cancer cases identified over a median follow-up of 11.7 years. We employed time-stratified Cox models, locally weighted scatterplot smoothing (LOESS) trajectory modeling, and hierarchical clustering to characterize protein dynamics relative to the time of diagnosis. A multi-algorithm machine learning pipeline was used to develop a predictive signature, and two-sample Mendelian randomization was performed to infer causal relationships. We identified 340 risk-associated proteins showing significant temporal heterogeneity. Long-term risk (>5 years pre-diagnosis) was linked to proteins like CEACAM5, indicating early dysregulation of cell adhesion. Imminent risk (<5 years) was marked by a surge in inflammatory proteins like IL6. These dynamics were resolved into four distinct trajectory patterns, creating a molecular timeline of carcinogenesis. A machine learning-derived 28-protein signature, integrated with clinical factors and Polygenic Risk Score (PRS), achieved outstanding predictive performance (AUC = 0.830). Mendelian randomization also suggested a causal role for some proteins of 340 risk-associated proteins in lung cancer development. Our findings establish that lung cancer evolves through a dynamic sequence of protein changes. This provides a new model for understanding pre-diagnostic disease, and our 28-protein signature is a powerful tool for precision screening to identify individuals with active disease progression.
The aim of this study is to understand the factors contributing to patients' non-adherence to lifestyle modification plans among visitors of the Lifestyle Clinics in King Abdul-Aziz Medical City, Jeddah. Adherence to these plans is crucial for improving health outcomes and preventing chronic diseases. A cross-sectional study was conducted at the Lifestyle Clinics within the Primary Healthcare department of King Abdulaziz Medical City, Jeddah. Participants were adults referred for weight reduction. Data were collected using a questionnaire covering sociodemographic characteristics, adherence to lifestyle modifications, and barriers to adherence. The adherence level was assessed using a validated 13-item questionnaire, and the data were analyzed using IBM SPSS Statistics. A total of 380 participants were included, with a median age of 42 years (IQR: 32-50 years). Approximately 45.5% were adherent to the lifestyle modification plan, while 54.5% were non-adherent. Significant positive correlations were found between age and adherence (Correlation Coefficient=.205, p<.001), with healthcare workers showing higher adherence levels (p=0.027). Common barriers to adherence included lack of willpower (74.5%), energy (70.8%), and time (68.9%). Statistically significant associations were identified between lack of energy (p=0.019) or time (p=0.023) and non-adherence. This study identified key factors associated with non-adherence to lifestyle modification plans, particularly younger age, non-healthcare occupations, and perceived barriers such as lack of energy and lack of time. Despite high levels of knowledge regarding healthy lifestyle practices, adherence remained suboptimal, highlighting the gap between awareness and behavioral implementation. Addressing practical barriers through targeted, behavior-focused interventions may improve adherence and long-term health outcomes.
Nanovibration, a kilohertz-frequency, nano-amplitude mechanical stimulation, has been shown to drive osteogenesis; however, the mechanisms remain unclear. Mechanotransduction has been proposed with limited cell population-level evidence. We propose the use of a high-throughput mechanical phenotyping technique, Real-Time Deformability Cytometry (RT-DC), to observe mechanical changes in an osteogenic model, MG63s. We have demonstrated that MG63 cells respond to the nanovibrational mechanical stimulation by changing their cytoskeletal morphology and showing higher expression of osteocalcin than respective controls. We have also demonstrated the first use of Real-Time Deformability Cytometry (RT-DC) to reliably phenotype whole-cell population mechanical response to this stimulus. The use of high-throughput microfluidic techniques such as RT-DC is proving invaluable to more accurately assay population morphological changes compared to other established techniques, with potential application in mechanobiology, cellular quality control, and diagnostic scenarios. With consideration for osteogenic changes, RT-DC also poses potential use in the assessment of in vitro and ex vivo bone cell samples, highlighting clinical relevance for conditions such as osteoporosis and bone fracture.
Ovarian cancer (OC) remains the most lethal malignancy within the spectrum of gynecological cancers globally. While protein S-palmitoylation has been extensively implicated in tumor progression, its specific functional contributions and molecular mechanisms in the context of OC pathogenesis remain to be fully elucidated. This article aims to explore the prognostic effect associated with palmitoylation in OC. In this study, palmitoylation-related genes (PRGs) were defined as genes encoding enzymes directly involved in the palmitoylation/depalmitoylation process, as well as genes whose functions, subcellular localization, or signaling are regulated by this modification. Based on this definition, PRGs comprising enzymes and regulated substrates, were identified from public transcriptomic databases. By intersecting ovarian cancer (OC)-associated and palmitoylation-linked differentially expressed genes (DEGs), candidate targets were pinpointed. A prognostic risk model was then constructed using LASSO and Cox regression analyses on the TCGA-OV cohort (N = 378) and validated in the GSE51088 cohort (N = 152). This model was integrated into a predictive nomogram and further characterized through pathway enrichment, immune infiltration, checkpoint analysis, drug screening, and mutation profiling. Finally, identified markers were validated via RT-qPCR in clinical samples. Through intersecting DEGs1 and DEGs2, we obtained 24 candidate biomarkers. Four PRGs (HSPG2, BRD4, RARRES1, and SCGB1D2) were identified to construct a prognostic risk model. The risk score, alongside ethnicity and tumor stage, served as an independent prognostic indicator, integrated into a robust nomogram. Mechanistically, high-risk cohorts were characterized by dysregulated ribosome and translation initiation pathways, altered infiltration of seven immune cell types, and significant variations in seven checkpoints (e.g., CTLA4, CD274). Additionally, the model predicted sensitivities for 131 drugs and captured a high TP53 mutation rate. RT-qPCR validation confirmed the upregulation of HSPG2, SCGB1D2, and BRD4, and the downregulation of RARRES1 in OC tissues, showing high consistency with bioinformatic predictions (P < 0.05). This study identified HSPG2, BRD4, RARRES1, and SCGB1D2, which served as prognostic markers reflecting the palmitoylation-related biological landscape in OC that could lay the foundation for innovative therapeutic strategies.
Understanding protein-ligand interactions at the molecular level remains a central theme in rational drug design and pharmacophore research. X-ray crystallography, NMR spectroscopy, and cryogenic electron microscopy (cryo-EM) are widely adopted experimental strategies for elucidating such interactions with atomic precision. However, these approaches often encounter practical limitations, including the poor diffraction quality of cocrystals in crystallography and lower throughput or resolution challenges in cryo-EM. In this study, we explored the potential of Raman spectroscopy as a complementary technique for investigating protein-ligand interactions, exemplified by the binding of the model protein lysozyme to the RNA-interacting alkaloid emetine. Our results revealed concentration-dependent Raman spectral changes in lysozyme crystals, including distinct emetine marker bands at 1612 cm-1 and 1363 cm-1. Subtle alterations in the vibrational modes associated with tryptophan residues further suggest their involvement in ligand recognition. The X-ray structure of the lysozyme-emetine complex confirmed these interactions, highlighting the contacts of aromatic residues W62, W63, and W123 and the associated conformational shifts. Despite these local changes, the overall lysozyme fold remained stable (Cα RMSD of 0.33 Å), consistent with the Raman spectra showing no significant perturbations of the helical secondary (1658 cm-1) or tertiary structure upon emetine binding. Together, these findings demonstrate the utility of Raman spectroscopy as a valuable complementary tool for assessing ligand incorporation within protein crystals and probing protein-ligand interactions.
Aging is the primary risk factor for sporadic Alzheimer's disease (AD). While amyloid-beta oligomers (AβOs) accumulation is a key neuropathological process in AD, their specific effects in aged brains and how aging modulates brain response to AβOs remains poorly understood. We investigated how aging contributes to AβO-induced neurotoxicity and cognitive deficits in mice. After biochemical and in vitro characterizations on primary cultures of cortical neurons, AβOs or their vehicle were intracerebrally injected into both 3- and 18-month-old wild-type mice. A broad spectrum of assays including synaptic markers, neuroinflammation, apoptosis and cognitive functions was used to establish a preliminary characterization of the interplay between age and AβOs. In vivo data were analyzed using a multifactorial design (Treatment × Age), with two-way ANOVA or other appropriate statistical models. Old mice had significantly reduced synaptic proteins SNAP-25 and PSD-95, elevated neuroinflammatory markers, and increased neuronal apoptosis in hippocampus and cortex, despite showing cognitive performances similar to young mice. All brain biomarkers were worsened after AβO injection in both young and old mice. Age and AβO effects either accumulated or interacted to promote neuroinflammation and apoptosis, depending on brain areas, whereas their effects on synaptic proteins were strictly additive. Moreover, AβO injection induced only mild spatial memory deficits in young mice, in contrast with those observed in old mice in both episodic and spatial memory tests. Whereas the young brain showed resilience to maintain memory performances after AβO injection, the coping capacities of the aging brain were exceeded by AβO effects. At the neurobiological level, age and AβO effects were mainly additive, but also acted synergistically in a brain region-dependent vulnerability pattern. This study highlights the value of incorporating aging into preclinical models to improve their translational validity and enhance their relevance for drug testing targeting early stages of sporadic AD.
Deficits in hippocampal-dependent memory tasks following lipopolysaccharide (LPS) administration are frequently reported. However, prior work has predominantly been conducted with males. Given recent reports showing sex-related differences in the cognitive effects of an acute LPS challenge, the present study evaluated whether sex differences in spatial learning and memory exist. Adult female and male C57BL/6J mice were evaluated for spatial learning and memory in a hidden platform version of the water maze following administration of LPS (0.25 mg/kg) prior to the first day of training. Results showed that LPS impaired spatial memory in males but had no effect on memory in females. Females and males showed differential use of behavioral search strategies during the probe trial, which may contribute to the selective vulnerability seen in males. Assessment of hippocampal and splenic inflammatory markers showed similar LPS-induced expression across the sexes, indicating that cytokine induction does not produce comparable cognitive deficits in males and females. However, males showed higher hippocampal expression of the interleukin type 1 receptor accessory proteins (AcP and AcPb) relative to females that may alter the impact of inflammation on the hippocampus. Ultimately, these data extend recent findings of sex-dependent effects of LPS on cognition to the water maze and emphasize the importance of reporting and comparing subjects' biological sex.
The practical application of Fe-N-C catalysts in proton exchange membrane fuel cells is fundamentally constrained by the inherent activity-stability trade-off. Here, we propose a "repair-and-upgrade" engineering strategy that not only repairs pyrolysis-induced defects through carbon and nitrogen supplementation but also evolves conventional FeN4 moieties into stabilized FeN5 configurations via an in situ constructed carbon bilayer. The axial nitrogen modulates the electronic structure of Fe center to enhance catalytic activity, while the adaptive interlayer spacing of the N-linked carbon bilayer compensates for fluctuations in the axial Fe─N bond length during catalysis, therefore anchoring the Fe active sites. When integrated into membrane electrode assemblies, the catalyst delivers a high peak power density of 1221 mW cm-2 and exhibits exceptional durability, retaining over 85% of its initial power density after 10,000 cycles in H2-O2 and showing negligible decay over 45 h at 0.6 V in H2-air tests. This work presents a novel design strategy for stable single-atom catalysts, centered on creating an adaptive local environment that ensures exceptional electrocatalytic stability.
Cis-regulatory elements (CREs) drive tissue- and cell-specific gene expression and are essential for safe, sustainable genetic control strategies in pest and vector insects, including the engineering of gene drives in the primary human-malaria vector Anopheles gambiae. Yet CREs remain poorly defined in mosquitoes due to limited computational tools and practical methods for identification and validation. We present a systematic in silico approach for CRE discovery, correlating targeted DNA-motif searches with gene expression, followed by frequency and distribution analysis within putative promoter regions. Applied to the A. gambiae germline, this approach identified hundreds of putative CREs significantly correlated with germline expression in one or both sexes, often linked to distinct sperm developmental stages and chromosomal locations, suggesting roles in broader regulatory mechanisms such as dosage compensation and meiotic silencing. When mapped onto pre-characterised germline promoters, CRE distribution aligned with regions associated with experimental expression patterns. Finally, we validated a top-ranked testis-enriched CRE using an in vivo dual-reporter assay, showing that mutation of conserved nucleotides drastically altered male germline expression. To the best of our knowledge this work provides the first nucleotide-resolution regulatory genome annotation of the A. gambiae germline, offering a transferable framework to aid promoter design for genetic control strategies against malaria mosquitoes and other insect pests.
Prenylated hydroxychalcones (xanthohumols) are hop-derived flavonoids with promising anticancer activity; however, their membrane interactions and structure-activity relationships remain incompletely understood. Here, xanthohumol C (XHC) and its semi-synthetic derivatives, 1″,2″-dihydroxanthohumol C (DHXHC) and 1″,2″-dihydroxanthohumol K (DHXHK), were evaluated for cytotoxic, pro-apoptotic, and membrane-modulating effects in comparison with xanthohumol (XH). In vitro antiproliferative activity against eleven human and one murine cancer cell lines yielded IC50 values in the micromolar range, with XHC showing the highest activity toward epidermoid carcinoma, urinary bladder carcinoma, and glioblastoma cells. Apoptosis induction was confirmed in MCC-13 Merkel carcinoma cells. Hemolytic activity toward human erythrocytes was concentration-dependent in the range of 10-100 μM, with XHC classified as toxic at 100 μM, while DHXHC and DHXHK were only slightly toxic. Membrane interactions were studied using fluorescence spectroscopy in cancer cell-mimicking lipid membranes. At low micromolar concentrations (0.5-5 μM), XHC and DHXHC increased DPH anisotropy, indicating membrane stiffening, while Laurdan generalized polarization decreased, consistent with enhanced interfacial hydration. In contrast, DHXHK showed negligible membrane effects. These results suggest that differences in molecular structure, including planarity, may contribute to the observed variation in membrane interactions and cytotoxic effects among xanthohumol derivatives.
The complexity and rapidly evolving nature of critical patient care in Intensive Care Units underscore the importance of the accuracy and timeliness of nursing decisions, further highlighting the significance of nursing education. This study aims to examine the accuracy of four generative artificial intelligence tools (ChatGPT 5.0 Plus, ChatGPT 5.0, DeepSeek, and Google Gemini) in answering multiple-choice questions related to the intensive care nursing exam, a fundamental area in nursing education. In the study, the ChatGPT 5.0 Plus, ChatGPT 5.0, DeepSeek, and Google Gemini models were evaluated using a test data set consisting of 55 questions. The questions were classified according to their difficulty levels as easy (n = 16), medium (n = 17), and difficult (n = 22). The models' correct response rates and standard or unique correct/incorrect response distributions were examined. Computer-assisted statistical analysis used the Chi-square, one-way ANOVA, and Post-hoc Tukey tests. The study was reported according to STROBE. According to the study results, the success rates of all models were similar for easy and medium-level questions (70-82%), and the difference between them was not statistically significant (p > 0.05). Under difficult questions, however, the performance of the models diverged significantly, with Google Gemini achieving the highest success rate at 77.27% and DeepSeek showing the lowest performance at 45.45%. The chi-square analysis revealed no statistically significant difference in the correct/incorrect distribution among the models (χ²=3.69; p = 0.296), but at the observational level, Google Gemini had a higher number of unique correct answers (n = 6) compared to the other models. ChatGPT 5.0 was found to have no unique errors. In conclusion, while AI models generally showed similar levels of success in intensive care nursing exam questions, Google Gemini demonstrated superior performance in difficult questions, and DeepSeek showed the lowest level of success among the models. The study provides an essential comparative framework regarding the usability of AI-based learning and assessment tools in nursing education. It offers guidance for the future development of AI-based educational technologies. Not applicable.
Metastatic melanoma is an aggressive, heterogeneous cancer with early spread and poor prognosis. Transcriptomic analysis identifies potential therapeutic targets. In silico analysis of the GEO dataset GSE7553 compared primary vs metastatic melanoma using differential expression, enrichment (GO/KEGG/Reactome), PPI network construction, and hub-gene prioritization. Candidates were validated through survival analysis, mutation-associated analyses, and virtual screening using molecular docking with FDA-approved compounds. Transcriptomic results show divergence between primary and metastatic melanoma samples, with principal component analysis supporting clear group separation. In a total of 54,675 probe-level entries, 4868 were classified as upregulated and 10,269 as downregulated, indicating a predominance of downregulated transcriptional events in metastatic melanoma. Prioritized upregulated genes included CUL5, ZC3H14, SON, BRCC3, and H3-3B, whereas notable downregulated genes included ZNF709, CD84, STARD8, EPOR, and HAVCR2. The high-confidence PPI network comprised 625 nodes and 2661 edges, with a significant enrichment score. Enrichment analysis implicated immune/adhesion and translation pathways (e.g., Rap1, focal adhesion, T-cell activation). Survival: CUL5 (HR = 0.26) and ZC3H14 (HR = 0.60) are protective, while SON (HR = 2.4) is adverse. Mutation-linked transcriptomic analysis identified 10 significantly altered genes, including downregulated SNHG18 and upregulated LPCAT2. Virtual screening results show repurposable compounds, with Floxacrine showing strong predicted affinity for CUL5 and Dihydroergocristine showing favorable interaction with LPCAT2/ZC3H14-related targets. In silico docking results further supported CUL5-Floxacrine and LPCAT2-Dihydroergocristine as notable candidate interactions. Results show key transcriptomic drivers and targets (CUL5, ZC3H14, SON, BRCC3, LPCAT2) in metastatic melanoma. Results highlight a useful hypothesis-generating framework for biomarker prioritization and drug repurposing in melanoma. However, independent cohort validation and experimental confirmation are required before clinical translation.
Clear cell papillary renal cell tumor (CCPRCT) has been reclassified from "carcinoma" to "tumor" in the latest World Health Organization (WHO) Classification in 2022 to better reflect its excellent outcome. Therefore, its distinction from its closest mimic, clear cell renal cell carcinoma (CCRCC), is now more clinically relevant than ever. The well-documented occurrence of tumors with significant overlapping morphologic and immunohistochemical features between CCPRCT and CCRCC has sparked a search for additional immunohistochemical markers that may aid in this distinction. Based on the putative distal nephron origin of CCPRCT, GATA binding protein 3 (GATA3) has been proposed as a sensitive and specific marker for this diagnosis, with only exceptional cases of CCRCC reported to show GATA3 reactivity. To the best of our knowledge, this is the first report of a molecularly-confirmed, low-stage, low-grade CCRCC showing overlapping histology with CCPRCT and an exceptional CCPRCT-like immunohistochemical profile, including diffuse nuclear expression of GATA3.