Search Results (1,330)

Immune remodeling within the tumor microenvironment (TME) influences the progression and clinical outcome of uterine corpus endometrial carcinoma (UCEC), but the contribution of chemokine-related regulatory genes remains incompletely characterized. This study aimed to evaluate the prognostic relevance of CXCL13 and its association with immune microenvironmental features in UCEC using publicly available transcriptomic and single-cell datasets. RNA-sequencing profiles and clinical annotations from 589 UCEC cases in The Cancer Genome Atlas (TCGA) were analyzed to assess TME composition using ESTIMATE (Estimation of Stromal and Immune cells in MAlignant Tumours using Expression data) and CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), followed by survival analysis, differential gene expression analysis, protein–protein interaction network construction, Cox regression, and gene set enrichment analysis. A public single-cell RNA-sequencing dataset from the Gene Expression Omnibus (GEO; GSE173682) was further used to infer the cellular sources of CXCL13. Elevated CXCL13 expression was associated with favorable overall survival and enrichment of immune-activation pathways. CIBERSORT-based analysis indicated that high CXCL13 expression correlated with increased estimated fractions of CD8⁺ T cells and plasma cells, together with transcriptional features related to tertiary lymphoid structure-associated immune activation, whereas several immunosuppressive cell populations showed lower estimated abundance. Single-cell analysis suggested that CXCL13 was mainly expressed by follicular helper T cells and exhausted CD8⁺ T cells. These findings indicate that CXCL13 may serve as a prognostic biomarker associated with an immune-active TME in UCEC. Further histological, spatial, and functional validation is warranted to confirm its mechanistic role and translational potential. Full article

To characterize the distinct expression profiles of microRNAs (miRNAs) in serum and tissue and to delineate the heterogeneity of their regulatory mechanisms in early-stage ovarian cancer (EOC), thereby identifying candidate biomarkers for non-invasive early diagnosis. Differentially expressed miRNAs were identified by integrating publicly available datasets of EOC tissues and serum samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Core miRNAs were subsequently screened through integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and feature importance ranking derived from optimized machine learning models. Protein–protein interaction (PPI) networks and functional enrichment analyses (GO and KEGG) were performed on predicted target genes to systematically compare the functional discrepancies between serum- and tissue-derived miRNAs. No overlapping core miRNAs were observed between the two compartments. Serum miRNAs exhibited an overall up-regulated trend, whereas tissue miRNAs were predominantly down-regulated. Although the regulatory pathways demonstrated significant heterogeneity, they ultimately converged on the cell cycle and the PI3K-Akt signaling pathway, indicating high functional homology. Furthermore, serum miRNAs are not merely passive leakage products from tissues; current evidence suggests they may be selectively packaged into exosomes to participate in tumor regulation. Despite divergent expression profiles, serum and tissue miRNAs share homologous regulatory functions in EOC. These findings suggest that serum miRNAs accurately reflect the core molecular status of tumor tissues, providing a robust molecular foundation for liquid biopsy-based early detection strategies. Full article

The anomalous magnetic moment of the tau lepton, $a_{\tau }$, represents a fundamental test of the Standard Model (SM) and a high-sensitivity probe for New Physics in the third generation of leptons. Due to the tau’s extremely short lifetime, traditional spin-precession measurements remain inaccessible, necessitating innovative experimental strategies at high-energy colliders. This review provides a comprehensive overview of the current experimental landscape, highlighting the recent paradigm shift from LEP-era constraints to the unprecedented precision reached at the LHC. We emphasize the importance of Ultra-Peripheral Heavy-Ion Collisions (UPCs), which act as a “photon-photon collider” of extreme intensity. By leveraging the $Z^4$ enhancement of the coherent photon flux in Lead–Lead ($PbPb$) interactions, these collisions provide a theoretically robust “quasi-static” environment. To interpret these developments, we first establish the general theoretical framework within the Standard Model Effective Field Theory (SMEFT). This allows us to critically compare the UPC results with the latest measurements from proton–proton collisions—including the recent CMS observation of the $\gamma \gamma \to \tau \tau$ process and the ATLAS constraints from the high-mass Drell–Yan tail—evaluating their complementarity and the challenges related to Effective Field Theory validity at the TeV scale. Finally, we outline the future prospects for $a_{\tau }$ at Belle II and the Future Circular Collider (FCC) stages. While FCC-hh in $PbPb$ mode provides a theoretically clean environment, its sensitivity remains limited to $\mathcal{O}\left({10}^{-2}\right)$. Conversely, the next generation of lepton facilities, specifically Belle II and FCC-ee, aims for the $\mathcal{O}\left({10}^{-5}\right)$ level, required to probe SM electroweak loop corrections. Long-term projections for a high-energy Muon Collider suggest a potential reach of $\mathcal{O}\left({10}^{-6}\right)$. Full article

The discovery of the Higgs boson in 2012 at the Large Hadron Collider marked a major milestone in our understanding of electroweak symmetry breaking. Since then, increasingly precise measurements by the ATLAS and CMS Collaborations, based primarily on proton–proton collision data at \(\sqrt{s}\) = 13 TeV corresponding to about 140 fb−1 per experiment, have confirmed its compatibility with Standard Model predictions within current uncertainties. The Higgs boson mass is now measured with a precision of about 0.08%, while its couplings to fermions and bosons are determined at the 7–20% level. The completion of the LHC programme and the High-Luminosity LHC, will probe Higgs boson couplings at the fewpercent level. However, sub-percent precision is required for stringent tests of the Standard Model, as any deviation would signal new physics beyond it. This strongly motivates future collider facilities, designed both as high-precision Higgs factories and, in many cases, as energy-frontier machines. Within the framework of the update of the European Strategy for Particle Physics, we discuss the physics case and main characteristics of the proposed particle collider options, highlighting their complementarity, technological challenges, and expected performance. The 2026 Strategy Update identifies the FCC-ee collider as the preferred next flagship project at CERN. Operating at the Z pole and at centre-of-mass energies between 240 and 365 GeV, it would enable model-independent, per-mille-level precision on Higgs boson couplings, while providing a pathway to a future high-energy hadron collider. The Higgs sector thus constitutes a central laboratory for precision tests of the Standard Model and for exploring the fundamental structure of our universe. Full article

Introduction: Studying the structure and dynamics of living communities is essential from both ecological and wildlife management perspectives. Objective: The main objective of this study was to analyze the fish community structure inhabiting different river sections across several basins in the Mediterranean area. The data collected here contributed to: (i) creating a regional and national reference inventory to assess ichthyological biodiversity; (ii) generating digital cartographic information on species distribution and potential habitats; and (iii) providing scientific data to update national legal protection for governments. Methodology: Fish assemblages were monitored using electrofishing, which ensures reproducible data and long-term comparability. The study period extended until autumn 2025, with intensive sampling at 30 sites across major water bodies in the Valencian Community and selected rivers in Mijares, Turia, Jucar and Palancia basins. Results: The results reveal notable ichthyological richness in the studied basins (Turia, Júcar, Palancia, Mijares), with 12 native species identified. Cyprinidae and Leuciscidae were the most representative families, both in species number and spatial distribution, consistent with their dominance in Mediterranean river systems. Areas with the highest species richness corresponded to the middle and lower river sections and to ecologically valuable coastal wetlands. However, the study also detected 10 invasive alien species, representing 45% of the total fish fauna recorded. This high proportion reflects the significant ecological alteration affecting rivers and wetlands in these basins and underscores the urgent need for management actions to limit the spread of invasive species and reduce their impact on native biodiversity. The most widespread IAS were the bleak (A. alburnus), mainly in the Júcar basin, and the mosquitofish (G. holbrooki), predominantly in coastal wetlands. Conclusions: This study contributes directly to updating the Atlas of Ichthyofauna of the Valencian Community, providing a robust and current information base to support environmental decision-making at regional and national levels. The findings highlight the importance of strengthening proactive conservation measures, particularly in areas where biodiversity is most vulnerable. Full article

We present the results of applications of ATLAS2.0 to experimental data in this final part of our series of publications. ATLAS2.0 retrieves particle microphysical parameters from multiwavelength Raman and high-spectral-resolution lidar measurements of backscatter (β) coefficients at three wavelengths, i.e., λ = 355, 532, and 1064 nm, extinction (α) coefficients at two wavelengths, i.e., 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, i.e., 355, 532, and 1064 nm, so-called 3β + 2α + 3δ datasets. The explicit use of PLDRs is a novel feature compared to all previously developed lidar data retrieval algorithms. For the tests of ATLAS2.0, we use data that were taken with NASA Langley Research Center’s airborne high-spectral-resolution lidar 2 (HSRL-2). We show the results of two case studies. We compare the particle microphysical parameters and single-scattering albedo (SSA) retrieved with ATLAS2.0 to results obtained with the first version of ATLAS, our Tikhonov regularization algorithm (TiARA), and in situ observations carried out aboard an aircraft that followed the airborne HSRL-2 instrument. The solutions converge within the retrieval uncertainties of these techniques. The discrepancy between the measured and backcalculated, i.e., retrieved 3β + 2α + 3δ data on average stays below 10%. The difference between the retrieved and measured PLDRs is, on average, even less. This comparably good convergence of the optical datasets (experimental versus backcalculated) of both measurement cases can only be achieved if the investigated aerosol particles are analyzed on the basis of a sphere-spheroid mixture. Full article

Background and Objectives: Surfactant protein B (SFTPB) is a surfactant-associated protein secreted by alveolar type II epithelial cells that plays a critical role in maintaining alveolar stability and surface tension. Although SFTPB is closely associated with pulmonary epithelial differentiation, its clinical significance in different non-small cell lung cancer (NSCLC) subtypes remains unclear. This study investigated the clinicopathologic and prognostic significance of SFTPB expression in lung adenocarcinoma (AD) and lung squamous cell carcinoma (SCC) using The Cancer Genome Atlas (TCGA) dataset. Materials and Methods: SFTPB mRNA expression data and clinicopathologic information were obtained from TCGA cohorts of AD and SCC patients. Patients were stratified into high- and low-expression groups according to median SFTPB expression levels. Associations between SFTPB expression and clinicopathologic variables were analyzed, and correlation analyses were performed with major oncogenic genes. Overall survival (OS) and relapse-free survival (RFS) were evaluated using Kaplan–Meier survival analysis and log-rank testing. Multivariate Cox proportional hazards regression analyses were performed after adjustment for age, sex, and pathological stage. Results: In AD, high SFTPB expression was significantly associated with lower pathologic stage (p = 0.011) and lower N stage (p = 0.006). SFTPB expression showed significant negative correlations with EGFR (R = −0.140, p = 0.002) and BRAF (R = −0.177, p < 0.001) and a positive correlation with TP53 (R = 0.128, p = 0.004). Patients with high SFTPB expression demonstrated significantly improved OS compared with those with low expression (p < 0.001), while a trend toward prolonged RFS was observed without statistical significance (p = 0.089). Multivariate analysis confirmed high SFTPB expression as an independent favorable prognostic factor in AD (HR = 0.551, 95% CI = 0.405–0.748, p < 0.001). In SCC, high SFTPB expression was also significantly associated with lower pathologic stage (p = 0.009) and lower N stage (p = 0.007). SFTPB expression showed significant negative correlations with SOX2 (R = −0.176, p < 0.001), PIK3CA (R = −0.143, p = 0.002), and TP53 (R = −0.101, p = 0.026). In contrast to AD, high SFTPB expression was significantly associated with poorer OS (p = 0.026), whereas no significant difference in RFS was observed (p = 0.307). Multivariate analysis demonstrated that high SFTPB expression was an independent adverse prognostic factor in SCC (HR = 1.347, 95% CI = 1.028–1.767, p = 0.031). Conclusions: SFTPB expression is significantly associated with clinicopathologic characteristics and molecular signatures in both AD and SCC. However, its prognostic implications differ according to histologic subtype. High SFTPB expression independently predicts favorable survival in AD but unfavorable survival in SCC, suggesting distinct lineage-specific biological roles in NSCLC. These findings support SFTPB as a subtype-specific prognostic biomarker reflecting differential differentiation states and lineage context in NSCLC. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is challenging to treat with chemotherapy. Immunotherapy has shown moderate responses in inflammatory and immunosuppressive tumor environments. Hepatic cytochrome P450 monooxygenases (CYPs) play a crucial role in xenobiotic and drug metabolism, as well as lipid and steroid metabolism. We aimed to investigate whether CYP expression and various parameters of the innate and adaptive immune system are prognostic factors for the survival of HCC patients. Methods: HCC biopsies (n = 370) from The Cancer Genome Atlas (TCGA) database were analyzed using Kaplan–Meier statistics and the KMPlotter algorithm. Parameters such as immune cell infiltration, DNA mutation rates, and neoantigen load were selected for survival analysis and subjected to hierarchical cluster analysis. The expression of candidate CYP genes in tumors was compared to that in normal liver tissues. Furthermore, tumor infiltration of innate immune cells (basophilic and eosinophilic granulocytes, natural killer cells), adaptive immune cells (CD4+ memory and CD8+ cytotoxic T cells, regulatory T cells, type 1 and type 2 helper T cells), and mesenchymal stem cells was examined. Results: High expression of CYP19A1 and CYP26B1 was associated with shorter survival, whereas high expression of CYP3A5, CYP3A43, CYP7A1, and CYP27A1 was linked to longer survival. Mutation rates combined with CYP expression showed a correlation with five out of six CYP genes, while a high neoantigen load produced less definitive results. A specific cluster exhibiting high CYP expression and immune cell counts or mutation/neoantigen rates was associated with shorter survival, while another cluster was linked to longer survival. Conclusions: CYPs involved in the metabolic regulation of HCC, including CYP3A5, CYP3A43, CYP7A1, CYP19A1, CYP26B1, and CYP27A1, were found to have prognostic value for patient survival. Combined signatures that include CYP expression, mutational rates, and immune cell infiltration into tumors further enhanced the prognostic value for patient survival. This suggests that CYPs may influence the creation of a tumor-specific metabolic microenvironment that impacts immune functions. These combined signatures could be utilized for patient stratification to personalize tumor treatment and develop novel combination therapies aimed at optimizing treatment outcomes, such as combining transarterial chemoembolization (TACE) with immune checkpoint inhibitors. Full article

Dormancy is an important factor influencing colorectal cancer (CRC) metastasis through diverse metabolic pathways and cell types. To elucidate its molecular mechanisms, bulk transcriptomic pathway scoring was integrated with single-cell RNA sequencing of epithelial, cancer stem, and immune cells to identify CRC dormancy-associated genes (CDAGs). Twenty-three CDAGs were identified. These genes were found to play a regulatory role in dormancy by participating in metabolic processes affecting energy supply or substance synthesis. In two independent CRC cohorts (GSE41258, GSE41568), machine learning models using these genes distinguished metastatic samples with area under the curve (AUC) of 0.79–0.87. High CDAG expression was associated with better recurrence-free survival in GSE41258 (p = 0.005), which remained significant after adjusting for age, sex, and adjuvant chemotherapy (p = 0.037). The prognostic value was validated in The Cancer Genome Atlas (TCGA) Colon and Rectal Cancer for progression-free survival (p = 0.004). Moreover, 20 CRC dormancy-associated drugs were identified, 12 of which were reported to be associated with CRC, two with experimental evidence of inhibiting CRC metastasis or recurrence. This study provided metabolic-oriented genes for characterizing CRC dormancy, which could distinguish metastatic samples and had independent prognostic value, and offered a foundation for further development of targeted therapeutic strategies. Full article

Soils are critical microbial habitats that support terrestrial ecosystem functioning and harbor numerous uncultured and functionally uncharacterized microbial groups. The black soil region in northeast China is a key agricultural ecosystem globally, yet the classification and functional understanding of its crucial microbial groups remain underexplored. In this study, we identified three high-completeness metagenome-assembled genomes (MAGs) from the Global Mollisols Genomic Atlas (GMGA). Phylogenetic and comparative genomic analyses identified these genomes as representing a novel evolutionary branch within the genus Flavobacterium, classified under the phylum Bacteroidota. Their novel taxonomic position is further supported by average nucleotide identity (ANI) and average amino acid identity (AAI) thresholds, demonstrating significant divergence from all known reference genomes. Functional annotation indicated that this species possesses strong plant polysaccharide degradation potential and a chemoheterotrophic lifestyle, together with environmental stress tolerance and a specialized nitrogen metabolic network adapted to agricultural inputs, thereby conferring a metabolic advantage in black soil environments characterized by high organic matter input and marked seasonal fluctuations. In addition, global distribution analysis showed that this lineage is widely distributed across diverse ecosystems and is significantly enriched in soil habitats, particularly in environments with fluctuating carbon sources and high organic matter inputs. The new species is most abundant in temperate soils, with the northeast black soil region of China emerging as a key hotspot. Based on these findings, and because no pure culture is currently available, we propose Candidatus Flavobacterium genomatis based on genome-resolved metagenomic evidence and in alignment with the International Code of Nomenclature of Prokaryotes rules for uncultivated prokaryotes. Our results expand the known species diversity of the genus Flavobacterium and suggest potential ecological roles of uncultured black-soil microbes in carbon and nitrogen cycling, including possible involvement in N₂O reduction under suitable environmental conditions. Full article

Background: Muscle-invasive bladder cancer (MIBC) is an aggressive disease with heterogeneous responses to neoadjuvant chemotherapy and emerging chemo-immunotherapy combinations. Reliable biomarkers to predict treatment responsiveness before therapy initiation are needed to guide patient selection. Objective: The objective of this study was to identify genomic and immune-related features associated with immune-active tumor phenotypes in MIBC using The Cancer Genome Atlas bladder cancer cohort (TCGA-BLCA). Methods: A retrospective bioinformatics analysis of TCGA-BLCA data was performed, evaluating gene expression, somatic mutations, tumor mutational burden (TMB), DNA damage response (DDR) gene status, and immune infiltration signatures. Immune enrichment metrics were derived from transcriptomic data. In the absence of direct treatment response data, a surrogate immune response classification was applied. Associations were analyzed using descriptive statistics and Firth’s penalized logistic regression. Results: Tumors classified as immune-high phenotype group based on immune-related features exhibited significantly higher global immune infiltration, including increased ImmuneScore and enrichment of cytotoxic and innate immune cells. In multivariable analysis, ImmuneScore was the only independent predictor of inferred responsiveness (p = 0.003). Conclusions: Global immune infiltration showed the strongest association with immune-active tumor phenotypes among the features examined in this TCGA-based analysis. These exploratory findings suggest that immune profiling may warrant further investigation as a component of tumor characterization in MIBC, pending validation in cohorts with clinical treatment and outcome data. Full article

Breast cancer is a heterogeneous disease characterized by substantial molecular diversity and variable treatment outcomes across patients. Despite advances in targeted and systemic therapies, anticipating individual benefit remains a major clinical challenge. In this context, Artificial Intelligence (AI) can support precision oncology by integrating high-dimensional molecular profiles with clinical and pharmacological information. Here, we present B.R.E.A.S.T. (Breast canceR Enhanced AI-Supported Therapy), an interpretable machine learning framework designed to predict therapy outcome from tumor proteomic profiles integrated with clinical and treatment annotations. Proteomic data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA) were harmonized with outcome and therapy information, and thirteen supervised classifiers were systematically evaluated using stratified 5-fold cross-validation. Therapeutic outcome labels were operationally defined by integrating available treatment response annotations with complementary clinical outcome information. Across both cohorts, ensemble-based models consistently achieved the most stable and highest discriminative performance, supported by learning-curve analyses and consistent behavior across independent datasets. To enhance interpretability, we implemented a two-step feature selection strategy combining model-specific importance measures with a global consensus ranking, enabling the identification of a compact set of robust proteomic biomarkers associated with therapeutic outcome. Top-ranked features mapped to molecular programs relevant to breast cancer progression and treatment sensitivity, including regulators of cell survival, DNA damage response, PI3K/AKT/mTOR signaling, and invasion-related processes. Re-evaluation using only the top 30 globally ranked features preserved high predictive performance across both independent breast cancer cohorts, indicating that a parsimonious proteomic signature captures core molecular determinants of outcome. Overall, B.R.E.A.S.T. provides a robust and generalizable proteomics-driven framework for modeling outcome-associated therapeutic response patterns and supporting biologically informed biomarker discovery in breast cancer. Full article

Rapid globalization is intensifying tensions between visitors and residents in heritage cities. In Madinah, which welcomed approximately 9 million visitors in 2024 and aims to reach 25 million by 2030, this pressure risks eroding residents’ lived experiences, daily rituals, routines, and social practices collectively understood as Intangible Cultural Heritage (ICH). This project seeks to identify and spatially map ICH to guide urban planners in protecting and enhancing culturally significant places amid rapid development. Despite its importance, few methodologies exist for systematically collecting and evaluating ICH. This research tests participatory methods for documenting ICH in Madinah through the “Living Heritage Atlas,” a project developed in collaboration with the Madinah Development Authority and Gehl. Between May and June 2025, four methods were deployed: semi-structured interviews, participatory mapping, an online survey, and a public engagement installation. Findings indicate that one-on-one interviews were most effective in capturing nuanced understandings of ICH, particularly in a context where cultural identity is deeply intertwined with religion. Other methods primarily raised ICH awareness rather than generating high volumes of data. The results further suggest that crowdsourced approaches to ICH documentation have mixed success without sustained public engagement to support broad and meaningful participation. Full article

Accurate mapping of forest canopy height is fundamental to modern forestry, providing essential structural data for biomass estimation and monitoring forest health. This study evaluates the broad usability of global (25 m) and high-resolution (1 m) Canopy Height Models (CHMs) by comparing them against temporally aligned Airborne Laser Scanning (ALS) reference layers from 2018 and 2024. At the 25 m scale, we evaluated four global products: Global Forest Canopy Height (GFCH), Global Map of Tree Canopy Height (GMTCH), High-Resolution Canopy Height model of Earth (HRCH), and Europe Temporal Canopy Height (EUCH). These satellite-derived models exhibit significant height-dependent limitations, systematically underestimating mature forest canopies (>30 m) by more than 15 m due to signal saturation, though EUCH and GMTCH performed moderately better. Transitioning to 1 m high-resolution data revealed a dramatic recovery in structural fidelity. A photogrammetrically derived model (PALS) achieved an RMSE of 4.89 m and a Mean Error (ME) of 1.86 m, demonstrating remarkable vertical stability across complex topography, even on slopes >25°. While coniferous stands produced higher absolute errors (RMSE = 6.75 m) than deciduous stands (RMSE = 6.19 m) due to spire-like architectures, PALS effectively captured fine-scale canopy textures. Experimental deep learning architectures, specifically the ArcGIS Living Atlas model, showed promise with an RMSE of 8.90 m, though out-of-the-box implementations struggle without local calibration. For forest disturbance monitoring, a distinct performance trade-off emerged. High-resolution photogrammetry (PALS) provided the highest overall precision for identifying clear-cuts (F1 = 0.353) but was conservative, capturing only 51% of the reference area. In contrast, the global HRCH model captured the total spatial footprint (103.9% of area) despite its geometric inaccuracies. The Living Atlas deep learning model offered the most balanced sensitivity, detecting 118.6% of the area with a competitive F1 score of 0.326. Ultimately, digital aerial photogrammetry provides a cost-effective solution for frequent operational updates, such as the two-year national mapping cycle in the Czech Republic. Full article

Background: The retention of Clinical Research Associates (CRAs) is critical for the integrity and sustainability of clinical trials in South Africa, an emerging hub for global clinical research. High CRA turnover threatens trial quality, data continuity, and site relationships, yet the context-specific drivers of turnover within the South African clinical research landscape remain poorly understood. This study explores the factors influencing job satisfaction and turnover intentions among CRAs to inform targeted retention strategies. Methods: A qualitative, interpretivist study was conducted using semi-structured interviews. Twelve CRAs with experience in South African Contract Research Organizations (CROs) were sampled on LinkedIn using purposive sampling. Data were analyzed iteratively using thematic analysis within Atlas.ti 26.0.1.33961 software, guided by Herzberg’s Two-Factor Theory and Mobley’s Turnover Model. Results: The analysis revealed a complex model of turnover drivers. Compensation was the most salient factor, operating not only as a hygiene factor but also as a direct motivator for job mobility in a competitive market. Unsustainable workload and a culture stigmatizing discussions of overload were key push factors. Intrinsic motivators were equally decisive: misalignment with therapeutic area preferences caused profound dissatisfaction, while alignment fostered engagement. Career growth manifested dual pathways: ambition for vertical progression and a redefined search for horizontal growth into roles offering greater work-life flexibility. Conclusions: CRA turnover is driven by an interplay of extrinsic pressures and intrinsic motivational deficits. To enhance retention, managers must adopt a multi-pronged strategy: implement market-competitive, well-being-oriented compensation; foster a culture that supports open workload dialogue; create transparent career architectures with dual progression tracks; and facilitate internal mobility across therapeutic areas. This study provides a foundational framework for developing context-sensitive retention policies, thereby contributing to the stability and quality of clinical research in South Africa. Full article