Search Results (699)

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article

Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP era (2015–2026) across five architecture families (MLP and physics-informed neural networks [MLP/PINN], long short-term memory [LSTM] and gated recurrent unit [GRU] networks, convolutional neural networks [CNN], convolutional LSTM and graph neural networks [GNN], and Transformer-based models) to establish an architecture–task-matching framework that links each family to its dominant estimation niche. The analysis reveals consistent specializations: MLP/PINN models achieve competitive surface SM retrieval from satellite inputs; recurrent networks extend SMAP temporally (RMSE ≤ 0.035  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); CNN disaggregates SMAP to 1 km (reported unbiased root-mean-square error (ubRMSE) approaching 0.04  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); ConvLSTM and GNN address spatiotemporal gap-filling (low reported ubRMSE 0.022  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); and Transformers enable global multi-source fusion and decadal climate-scenario projection. Across all families, four physics-DL integration modes (hard architectural constraints, soft loss-function penalties, physics-as-input feature engineering, and physics-ML hybrid output fusion) consistently yield RMSE reductions of 8–50% relative to data-driven baselines. These findings provide a practitioner-oriented framework that is applicable to ecohydrological monitoring of plant water stress, agricultural drought, early flood warnings, and land–atmosphere coupling. Full article

Declining fertility and the shift away from three-generation co-residence have weakened traditional family-based child-rearing support in Japan, drawing attention to the role of networks in local communities. Using data from a citizens’ attitude survey in a regional Japanese city (Ube City; n = 1293), this study examines how residents’ perceptions of community networks—specifically mutual support and community vitality—are associated with satisfaction with the local child-rearing environment. Conventional mean-based approaches risk overlooking qualitative heterogeneity in such evaluations. Using cross-sectional secondary data, we combined multiple regression with latent class analysis (LCA) to explore this heterogeneity. Findings show that perceptions of community networks were positively associated with satisfaction across all models (composite index b = 0.412, p < 0.001). The strongest associations were with “learning opportunities” and “community-wide education” (b = 0.477–0.478). Three-generation households did not uniformly report higher satisfaction compared with nuclear-family households. The exploratory LCA identified three qualitatively distinct patterns, with an “evaluation-pending” group (intermediate scores, frequent “don’t know” responses) comprising over 40% of respondents. This heterogeneity suggests that average scores may obscure the experiences of those directly engaged in child-rearing and that municipal surveys could include items identifying respondents’ basis for evaluation. Full article

Prostate adenocarcinoma is characterized by substantial inter-patient heterogeneity, limiting the clinical reliability of conventional diagnostic tools, including prostate-specific antigen testing. This limitation underscores the need for robust molecular biomarkers that may complement conventional diagnostic tools, highlighting the urgent need for biomarkers capable of enhancing diagnostic accuracy and enabling more precise risk stratification. In the present study, transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using an integrative bioinformatics and machine learning pipeline., The proposed workflow was designed as a stepwise and reproducible biomarker prioritization framework in which differential expression analysis, functional enrichment, protein–protein interaction (PPI) based network interpretation, graph-convolutional feature selection, and hybrid ensemble machine learning were sequentially integrated. Differential gene expression analysis was combined with pathway enrichment (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome), protein–protein interaction network construction, and graph-convolutional feature selection. Multiple machine learning algorithms, including Random Forest, Gradient Boosting Machine, Support Vector Classifier, Artificial Neural Network, and AdaBoost, were systematically evaluated. A hybrid ensemble model integrating Gradient Boosting Machine and Random Forest (GBM+RF) was subsequently developed. Model performance was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic (ROC) and externally validated using the independent GSE14206 dataset. The analysis revealed a coordinated molecular pattern characterized by dysregulated cell cycle activity and enhanced interferon-mediated immune signaling. Protein–protein interaction analysis identified STAT1 and PLK1 as highly connected network hub genes within immune-related and cell-cycle-associated modules. Among the evaluated models, the hybrid GBM+RF framework achieved the highest predictive performance on the TCGA dataset, with AUC: 0.9526; Accuracy: 97.49%. External validation using the GSE14206 dataset confirmed the robustness of this model (AUC: 0.9156; Accuracy: 91.53%). These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance. The proposed framework provides a robust and transferable strategy for biomarker discovery and precision oncology. Full article

Histone-modifying enzymes and histone post-translational modifications (PTMs) play key roles in the organization (euchromatin versus heterochromatin) and function (active versus silenced genes) of chromatin. The abundance and activity of these enzymes, along with their associated histone PTMs, are often altered in cancer cells, leading to deregulated gene expression. The expression of the KMT2A-MLLT3 protein, resulting from a chromosomal translocation in mixed-lineage leukemia (MLL), a subtype of acute myeloid leukemia, augments transcription elongation, promoting the expression of HOXA9 and MEIS1, genes that play critical roles in MLL development. Bortezomib, a proteasome inhibitor, has been effective at treating various cancers. In this study, we compared the impact of bortezomib on histone PTMs in the MLL cell line MOLM-13 and the chronic myeloid leukemic (CML) cell line K562. We report that MOLM-13 had a greater level of histone H2B monoubiquitinated at lysine 120 (H2BK120ub) and histone H3 dimethylated at lysine 79 (H3K79me2) (modifications involved in elongation) and similar levels of histone H2A monoubiquitinated at lysine 119 (H2AK119ub). Bortezomib treatment resulted in significant reductions in H2BK120ub and H2AK119ub levels, as well as in transcript levels of genes involved in MLL development. Full article

Growth traits, as core economic indicators in pig breeding, are closely associated with production costs, rearing duration, and final carcass quality and have thus consistently been a major focus of genetic improvement. This study aimed to identify candidate genes affecting Age to 120 kg live weight (AGE120), Backfat thickness at 120 kg (BF120), and Loin muscle depth at 120 kg (LMD120) in pigs. Ear tissue samples were collected from 3364 healthy adult pigs (including 558 boars and 2805 sows) from three breeds: Large White, Landrace, and Duroc. Genotyping was performed using an 80 K functional site array, and quality-controlled SNP (Single-Nucleotide Polymorphism) loci were subjected to genotype imputation, resulting in 15,447,611 loci obtained. Genome-wide association studies (GWASs) for Age to 120 kg live weight, Back fat thickness at 120 kg, and Loin muscle depth at 120 kg were conducted using a mixed linear model in Genome-wide Complex Trait Analysis (GCTA). Genes located within 500 kb upstream and downstream of significant GWAS loci were extracted using the biomaRt package in R. Furthermore, colocalization analysis was performed using expression Quantitative Trait Locus (eQTL) data of 34 tissues from the PigGTEx database to identify genes that share the same causal variant as the GWAS signals. Through integrated GWAS and eQTL colocalization analysis, in addition to five previously reported genes associated with pig growth traits (TAF11, ZC3HAV1L, ANKS1A, USP20, and TBC1D1), a set of novel, high-confidence candidate genes was identified: ZNF215, UBE2Z, HOXB7, SARDH, ADAMTSL2, ATP6V0A4, RPL10A, PGM2, and RELL1. These findings enrich our understanding of the genetic architecture underlying growth traits in pigs at heavy body weights and provide an important foundation for subsequent functional validation and molecular breeding applications. Full article

Hexavalent chromium Cr(VI) is a highly toxic and mobile contaminant commonly detected in industrial effluents and groundwater, requiring efficient and scalable treatment strategies. In this study, a commercial unmodified biochar (UB) and an iron-modified biochar (IMB) were evaluated for Cr(VI) removal from aqueous solutions. Iron modification via FeCl₃ impregnation and alkaline precipitation (pH 9) increased surface iron content from 0.2% to 3.3%, based on Energy Dispersive X-ray (EDX) analysis and extractable Fe from 0.009% to 0.108% (FerroVer). X-ray Diffraction (XRD) analysis suggested the presence of iron-containing phases and Fourier Transform Infrared (FTIR) analysis indicated the appearance of an Fe-O band at 564 cm⁻¹. BET analysis showed a slight decrease in surface area from 359 to 317 m² g⁻¹, consistent with partial pore blockage following iron modification. Batch adsorption experiments (pH 2–10; initial Cr(VI) concentration 5–600 mg L⁻¹; adsorbent dosage 2 g L⁻¹) revealed a maximum Langmuir adsorption capacity of 158 mg g⁻¹ for IMB, nearly double that of UB (82 mg g⁻¹), with optimal performance at pH 4–6. At equilibrium, removal efficiencies of ~60% and ~80% were obtained for UB and IMB, respectively (C₀ = 100 mg L⁻¹; adsorbent dose = 2 g L⁻¹). Kinetics followed a pseudo-second-order model, with IMB reaching equilibrium within 8 h compared to 50 h for UB. Isotherm analysis is consistent with Langmuir behavior for UB and Freundlich behavior for IMB. The improved adsorption performance of IMB is likely associated with the increased iron content introduced during modification, demonstrating its potential as an effective adsorbent for Cr(VI) removal from water. Full article

Urban blue–green spaces (UBGSs) serve as irreplaceable ecological infrastructure that underpins ecosystem service provision and human well-being improvement in densely populated urban regions. Based on 10-year (2014–2023) Landsat 8 OLI remote sensing imagery, this paper systematically investigates the temporal–spatial evolutionary characteristics and potential socioeconomic and policy-related associations with UBGS changes in the Shandong Peninsula urban agglomeration (SPUA), a pivotal coastal urban agglomeration in eastern China. The results demonstrate that the total UBGSs in the SPUA exhibited a pronounced increasing trend throughout the study period: the area of urban green spaces (UGSs) expanded from 28,311.66 km² to 30,194.39 km², while urban blue spaces (UBSs) grew from 1108.02 km² to 1699.04 km². Concurrently, the ecological quality of UGSs has markedly improved, with NDVI showing a significant upward trend in over one-third of the built-up areas, and vegetation greenness in cities such as Binzhou, Jinan, and Zibo increasing by more than 35%. Landscape pattern analysis reveals that the spatial structure of UGSs has transformed from a fragmented and scattered distribution to a centralized and contiguous layout. Specifically, the aggregation index (AI) and largest patch index (LPI) increased overall, while the landscape shape index (LSI) decreased by approximately 18.3%, indicating that the connectivity and structural integrity of urban green spaces have been substantially enhanced. National strategic policies, particularly the outline of ecological protection and high-quality development planning for the Yellow River basin, have effectively alleviated the encroachment pressure of population agglomeration and economic expansion on UBGSs, and played a decisive regulatory role in promoting the structural optimization of blue–green spaces. These findings provide empirical evidence for cross-city collaborative planning and integrated ecological governance of blue–green spaces at the urban agglomeration level, and offer valuable reference for achieving sustainable urban development in other rapidly urbanizing areas. Full article

Background: Colorectal cancer (CRC) remains a heterogeneous disease, and improved biomarkers are needed to support prognostic assessment. This study aimed to characterize hub genes in CRC and evaluate whether a gene signature provides biologically meaningful and prognostic information in clinical–genomic models. Methods: We integrated three GEO microarray datasets (GSE110223, GSE110224, and GSE23878) to identify common differentially expressed genes using adjusted $p<0.05$ and $\mid {\mathrm{l}\mathrm{o}\mathrm{g}}_{2}FC\mid >1$. Hub genes and protein expression were identified through protein–protein interaction network analysis using maximal clique centrality and Human Protein Atlas, respectively. Prognostic relevance was evaluated in TCGA-COAD/READ using Kaplan–Meier analysis, multivariable Cox regression, Cox-derived prognostic indices, time-dependent ROC analysis, and regression-based machine learning for internal robustness. Principal component analysis (PCA) was used to derive a standardized PC1-based score from the 10-hub gene signature. Results: A ten-gene mitotic hub signature (TPX2, UBE2C, AURKA, NEK2, PRC1, CCNB1, CDK1, CEP55, FOXM1, and RRM2) was consistently upregulated across the three datasets and enriched for cell-cycle and mitotic pathways. Protein-level and survival analyses supported the biological relevance of several hub genes. In TCGA-COAD/READ, the signature showed limited standalone prognostic value and did not retain independent significance after adjustment for clinical variables, although it contributed modestly in integrated clinical–genomic models. PCA showed a one-dimensional signature, with PC1 capturing the dominant shared expression pattern. Gradient Boosting Regressor (R² = 0.8035, MSE = 0.0473) supported the internal robustness of the DEG-based expression pattern. Conclusions: The ten-gene mitotic hub signature represents a coherent CRC-related proliferative program with limited value as an isolated prognostic marker, but it may still be useful as part of integrated risk models that require external validation. Full article

Cybersecurity insider threats remain a significant challenge for modern organisations due to their potential to cause substantial financial and reputational damage. This paper presents a systematic review of insider-threat research (2019–2026) using the PRISMA methodology and introduces an empirically validated ensemble framework for insider-threat detection. The proposed approach combines User-Based Sequences (UBS), a self-supervised Transformer trained on next-token prediction and time-gap modelling, and an unsupervised anomaly detection ensemble operating on model-derived behavioural features. An answers directory is incorporated to provide grounded truth for insider entities and episodes within the CERT r6.2 dataset, enabling direct validation of detection outcomes. The framework integrates behavioural theory with machine-learning techniques to improve understanding of insider-threat precursors. Evaluation was performed using a seven-stage Isolation Forest ensemble incorporating multimodal behavioural and technical data streams. The approach successfully identified all insider users, achieving 100% recall and an AUROC of 0.93. Comparative analysis against a previously reported model showed comparable AUROC and perfect recall despite differences in evaluation methodology. While precision remained low (0.004) due to the extreme class imbalance in the full CERT r6.2 population (5 insiders among 4000 users), the results highlight the operational challenges of insider-threat detection in realistic enterprise environments. This research contributes a novel, reproducible framework that combines behavioural theory and advanced machine learning to support the detection and analysis of insider threats. Full article

Crimean–Congo hemorrhagic fever virus (CCHFV) remains a major public health threat due to its high mortality rates and the absence of approved antiviral therapies. The viral ovarian tumor (OTU) protease is a critical virulence factor that suppresses host innate immunity through its deubiquitinase activity, making it an attractive therapeutic target. In this study, we employed a structure-guided approach to identify and validate novel small-molecule inhibitors targeting the non-catalytic Y89-W99 pocket of the OTU protease. Recombinant OTU protease was successfully expressed, purified, and refolded, yielding a soluble and enzymatically active protein. Cellular assays confirmed that the enzyme retains robust deubiquitinase activity, significantly reducing global ubiquitin conjugates in mammalian cells. In silico analysis of a putative DUB inhibitor library identified several candidate inhibitors with favorable binding interactions within the Y89-W99 pocket. Biochemical validation using a fluorometric Ub-AMC assay revealed that multiple small molecules strongly inhibit OTU activity, including OTUi-10 (~93% inhibition), OTUi-13 (~87%), OTUi-1 (~85%), OTUi-4 and OTUi-11 (~81%), and OTUi-9 (~76%). Additional moderate inhibitors included OTUi-12 (~67%), OTUi-19 and OTUi-21 (~66%), and OTUi-5 (~57%). In silico drug-likeness and toxicity profiling filtered the library to four fully compliant candidates, OTUi-4, OTUi-10, OTUi-11, and OTUi-12, all free of predicted toxicity alerts. These findings suggest that the Y89–W99 pocket may be a pharmacologically relevant site worthy of further investigation and identify OTUi-10, OTUi-4, and OTUi-11 as promising preliminary hit compounds. The results also provide initial insights that may guide future optimization and mechanistic studies of OTU protease inhibitors targeting CCHFV. Full article

Background/Objectives: Multilevel lumbar spinal stenosis (MLSS) is frequently encountered in patients undergoing surgery for lumbar spinal stenosis, yet the optimal extent of decompression remains debated. While multilevel decompression (MLD) may address all radiological stenotic levels, it may also increase surgical invasiveness and operative time. Minimally invasive endoscopic techniques such as unilateral biportal endoscopy (UBE) allow for targeted decompression and facilitate staged surgical strategies. The aim of this study was to evaluate the clinical outcomes of selective single-level decompression (SLD) using UBE in patients presenting with MLSS. Methods: This retrospective monocentric observational study included consecutive adult patients with MLSS who underwent decompression using UBE between December 2022 and July 2025. MLSS was defined as the presence of at least two lumbar levels with Schizas grade B or higher stenosis. Patients undergoing prior lumbar surgery or presenting with non-degenerative pathology were excluded. Patients underwent either SLD targeting the symptom-driving level or MLD, depending on the surgical strategy. Patient-reported outcomes included the Oswestry Disability Index (ODI), lumbar visual analog scale (LVAS), and radicular visual analog scale (RVAS). Results: Among 305 patients operated on for lumbar spinal stenosis, 83 (27%) presented with MLSS and were included in the study. Seventy-four patients (89%) underwent initial SLD and nine (11%) underwent MLD. Among patients treated with SLD, 9 (12%) required a second decompression during follow-up, whereas 65 patients (88%) achieved favorable outcomes without further surgery. Across the entire cohort, ODI, LVAS, and RVAS improved significantly after surgery. Operative time was significantly longer in the MLD group (122 ± 28.1 min vs. 58.1 ± 12.0 min; p < 0.001). These findings support the feasibility of a symptom-driven selective decompression strategy for MLSS using UBE. In our cohort, most patients experienced meaningful functional improvement after SLD without requiring additional surgery. Although a staged approach may necessitate secondary intervention in a minority of patients, selective decompression may help limit surgical extent in carefully selected patients while preserving favorable clinical outcomes. Conclusions: Selective SLD using UBE was associated with significant clinical improvement in most patients with MLSS while reducing operative time and surgical extent. A stepwise strategy targeting the dominant symptomatic level may represent a feasible minimally invasive approach for selected patients with MLSS. Prospective studies are needed to confirm these findings. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r² = 0.04–0.24, ubRMSE = 0.09–0.13 m³/m³), while SMAP performed better (r² = 0.18–0.62, ubRMSE = 0.05–0.07 m³/m³) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r² = 0.73; PM: R² = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r² = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. Full article

Objective: This study aimed to compare the clinical and radiological outcomes between microscopic and unilateral biportal endoscopic (UBE) posterior cervical foraminotomy (PCF). Methods: This study included 73 patients who underwent microscopic PCF (n = 40) or UBE PCF (n = 33) for single-level cervical foraminal disc herniation or stenosis between January 2018 and December 2021. Clinical outcomes were measured using the Visual Analog Scale (VAS) and Neck Disability Index (NDI). Radiologic outcomes were evaluated with cervical range of motion (ROM) using computed tomography and flexion-extension dynamic radiography. Results: The mean follow-up period for microscopic and UBE PCF was 33.0 ± 7.6 months and 29.9 ± 5.9 months, respectively. The postoperative neck VAS until postoperative 2 weeks was significantly lower in the UBE PCF group than in the microscopic PCF group (p < 0.05). The estimated blood loss and operative time were significantly lower in the UBE PCF group than in the microscopic PCF group, while the length of hospital stay was numerically shorter but did not reach statistical significance. The two groups had no significant difference in the NDI on the preoperative and postoperative 3 months. The recurrence occurred in 1 patient (2.5%) of the microscopic PCF group and 1 patient (3%) of the UBE PCF group. The revision surgery was performed in 2 patients (5%) of the microscopic PCF group and in 1 patient of the UBE PCF group. There were no significant differences in motion and instability between the two groups. Conclusions: Both microscopic and UBE PCF are effective and safe procedures for treating cervical radiculopathy due to cervical foraminal disc herniation or stenosis. The UBE approach may provide advantages mainly in early postoperative recovery, including lower early postoperative neck pain, while long-term clinical and radiologic outcomes appear comparable to those of microscopic PCF. Full article