Abstract: Airway management remains one of the few domains of anaesthetic practice still governed by universal, dichotomous screening thresholds applied to profoundly heterogeneous populations. Conventional bedside tests — the modified Mallampati test, thyromental distance and the upper lip bite test — retain modest and inconsistent diagnostic accuracy, leaving the majority of difficult intubations unanticipated while generating false alarms that drive resource-intensive contingency plans. This narrative review reframes preoperative airway evaluation as a problem of individualized risk stratification rather than population screening. We first summarize the performance limits of classical bedside predictors and the reasons for their ceiling effect, including low target prevalence, inter-observer variability, spectrum bias and heterogeneous outcome definitions. We then examine the data streams reshaping the field: point-of-care upper airway ultrasound, whose parameters provide operator-accessible and objective anatomical information; cross-sectional and craniofacial imaging; and machine-learning models based on clinical variables, facial images and voice analysis. For each we discuss accuracy, reproducibility, validation status and barriers to adoption. We then propose a phenotype-oriented framework in which assessment intensity is matched to pre-test probability, with dedicated pathways for obesity and obstructive sleep apnoea, obstetric, head-and-neck oncological and post-radiotherapy, critically ill and emergency, and paediatric populations. We map this framework onto the 2025 European Society of Anaesthesiology and Intensive Care and Difficult Airway Society recommendations and outline the validation, calibration and equity requirements — including algorithmic bias across skin tone, sex and ethnicity — that must be met before precision airway assessment can enter routine practice.

Abstract: Background: Cannabis use is common among individuals with psychiatric disorders, but its relationship with anhedonia and reward-processing deficits remains unclear. Anhedonia is multidimensional, encompassing anticipatory and consummatory pleasure, motivation, reward learning, and neurobiological signatures such as blunted striatal dopamine release and reduced reward-related activation in mesocorticolimbic regions. THC, the primary psychoactive constituent of cannabis, acts directly on CB1 receptors within this circuitry. However, findings on how THC-specific cannabis use affects anhedonia and reward-related outcomes across psychiatric populations are inconsistent, varying by dose, frequency, and use pattern. This systematic review synthesizes current literature on THC-specific cannabis use and anhedonia or reward-related outcomes across psychiatric disorders. Methods: Following PRISMA 2020 guidelines, Medline, PsycINFO, and Embase were searched through March 2026. Eligible studies included experimental, quasi-experimental, and observational designs assessing cannabis exposure in populations with clinical or subclinical psychiatric disorders or symptoms, reporting at least one subjective, behavioral, or neurobiological measure of anhedonia or reward processing (e.g., anticipatory/consummatory pleasure, effort-based motivation, reward responsiveness, or neural reward activity). Results: Twenty studies met inclusion criteria, spanning psychosis-spectrum disorders (13 studies, N = 53,043), MDD/depressive symptoms (8 studies, N = 4,657), bipolar disorder (1 study, N = 103), and anxiety symptoms (1 study, N = 153). In schizophrenia and first-episode psychosis, longitudinal studies consistently linked cannabis use to reduced intrinsic motivation and blunted neural reward sensitivity, while cross-sectional findings were mixed. Among clinical high-risk youth, cannabis use was inversely associated with baseline social anhedonia but did not predict subsequent change. In MDD, cannabis use disorder—rather than general use—predicted greater odds of anhedonia onset over 3–6-year follow-up; five of seven moderate-to-high-quality studies found significant associations. Abstinence/treatment findings were mixed: a 28-day abstinence paradigm reduced self-reported anhedonia, while reduced cannabis use during CBT/MET was linked to reduced ventral striatal reward activation despite depressive symptom improvement. The bipolar study found no association with physical anhedonia; the anxiety study found greater cannabis severity linked to anticipatory but not consummatory anhedonia. Conclusions: Evidence most consistently implicates CUD in worsening motivational and anticipatory reward deficits in schizophrenia and MDD. Findings for bipolar and anxiety disorders are limited. Methodological heterogeneity underscores the need for longitudinal, mechanistic studies clarifying causal pathways and disorder-specific vulnerabilities.

Abstract: Background/Objectives: Maternal mortality remains a critical indicator of health system performance and social inequality, with the reduction of the Maternal Mortality Ratio (MMR) established as an international priority under the Sustainable Development Goals. In Brazil, despite three decades of health system reforms, the reduction in maternal mortality has occurred unevenly across sociodemographic groups. This study aimed to analyze temporal trends in maternal deaths in Brazil from 1996 to 2025, examining variation by race/color, educational attainment, marital status, type and timing of death, and underlying ICD-10 chapter. Methods: This was an observational, descriptive, and analytical ecological time-series study, conducted according to STROBE recommendations. Secondary, publicly available, aggregated data on maternal deaths (ICD-10 codes O00–O99) among women aged 10–49 years were obtained from the Brazilian Mortality Information System (SIM/DATASUS) covering all Brazilian states from 1996 to 2025. Descriptive analyses of absolute and relative frequencies were performed for each sociodemographic and clinical variable across the study period. Results: Underreporting of race/color declined markedly, from 97% "unknown" in 1996 to residual levels in recent years, revealing a growing predominance of Brown women and persistent overrepresentation of Black women. Single marital status and stable unions increased over time, while married status declined. A marked, transient reversal occurred in 2020–2021, when indirect maternal deaths surpassed direct causes (64% vs. 34%), coinciding with the COVID-19 pandemic, before reverting to the historical pattern by 2022. Chapter XV of the ICD-10 accounted for nearly all deaths throughout the series. Conclusions: Improvements in data completeness did not eliminate persistent racial, educational, and social disparities in Brazilian maternal mortality, reinforcing the need for equity-focused obstetric policies alongside continued strengthening of surveillance systems.

Abstract: Reliable detection of Salmonella is essential for public health surveillance and food safety, particularly given the diversity of circulating serovars and the potential limitations of individual molecular targets. This study comparatively evaluated six candidate targets (hilA, invA, phoP, fimA, rpoS, and spvA) and developed a hilA-targeting TaqMan real-time PCR assay for Salmonella detection. To the best of our knowledge, this is the first study in Kazakhstan to apply a comparative target-selection approach to the development of a hilA-based real-time PCR assay. Among the targets evaluated, hilA demonstrated the broadest amplification coverage across the tested isolate panel, including less common serovars, whereas invA was not amplified in a subset of iso-lates. The developed assay demonstrated limits of detection of 10² CFU/mL for bacterial suspensions and 10 fg/μL for genomic DNA, with an amplification efficiency of 93.4% and an R² of 0.9988 over the linear range from 1 ng/μL to 10 fg/μL. No cross-reactivity was observed within the tested non-target bacterial panel. All 24 whole-genome sequencing-confirmed Salmonella isolates yielded positive amplification within the predefined Ct threshold (≤37), including S. enterica serovar Dublin strain 25S, which showed the highest mean Ct value (36.66 ± 0.14). The Proteus mirabilis comparator, initially identified as Salmonella by conventional bacteriology, showed no specific amplification. Salmonella DNA was also detected in both artificially contaminated food matrices tested. These findings support hilA as a promising molecular target and demonstrate the potential of the developed assay for laboratory detection, food safety investigations, and epidemiological surveillance. Further validation using broader isolate collections, naturally contaminated samples, and independent laboratories is warranted.

Abstract: Transportation network disruptions can abruptly alter travel costs and trigger day-to-day route-choice adjustments. Conventional day-to-day dynamic traffic assignment models, however, often rely on fixed-weight or uniformly decaying memory structures, limiting their ability to represent traveller learning under non-stationary conditions. This study develops a Bayes with Adaptive Memory (BAM)-based framework that selectively retains, down-weights, or discounts past travel-cost experiences according to recency and statistical significance. The BAM mechanism is integrated into a link-based day-to-day traffic assignment model to examine how adaptive memory influences perceived costs, route-choice adjustment, and post-disruption traffic-flow evolution. Numerical experiments on a hypothetical network show smoother flow trajectories, faster stabilisation, and a 41.2% reduction in mean absolute perception-tracking error compared with a conventional fixed-weight model. Under a link-removal scenario, the framework captures immediate traffic redistribution followed by gradual stabilisation toward a new post-disruption state. Sensitivity analysis reveals a trade-off between responsiveness and stability: shorter memory improves cost tracking, whereas longer memory and stronger significance weighting promote smoother adjustment and earlier convergence. Within the tested setting, the results suggest that adaptive memory provides a behaviourally plausible mechanism for representing traveller learning and may support disruption-management assessments of adjustment periods, congestion redistribution, and information provision.

Abstract: Cryptographic inventories and runtime detection can identify declared or observed cryptographic use, but they do not by themselves establish whether the exact implementation exhibited externally specified behavior or whether an evidence-backed use satisfies explicit policy. We present a deterministic cryptographic assurance composition that connects admitted runtime behavior to provenance-closed evidence, exact implementation identity, separately bound conformance against Contract-registered NIST test vectors, content-addressed policy appraisal, and explainable ACCEPT, REJECT, or REVIEW verdicts. Controlled SHA-256 and AES-256-GCM workloads produced 12 authentic runtime executions and six provenance-distinct occurrences. The frozen implementation subjects produced expected outputs for all 505 registered inventory entries—130/130 SHA-256 and 375/375 AES-256-GCM ENCRYPT—yielding two authentic conformance results. Under a policy frozen before authentic appraisal, all six occurrences followed the positive path to ACCEPT; controls independently demonstrated REJECT for supported policy violations and REVIEW for missing or out-of-coverage evidence. Conformance and appraisal/verdict evidence reconstructed byte-identically in 10/10 replays; 12/12 conformance controls and 25/25 appraisal, REVIEW, and anti-fabrication controls passed. No raw AES key or registered direct encoding was detected within the scanned retained-artifact boundary. These results demonstrate a reproducible bounded assurance chain across two cryptographic classes, without claiming exhaustive correctness, CAVP/CMVP validation, FIPS certification, generalized discovery, regulatory compliance, or production authorization.

Abstract: With the rapid advancement of quantum computing technology, IoT edge devices widely deployed in industrial settings are facing severe threats to their quantum-resistant security. this work focuses on the challenges of adapting Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) for resource-constrained environments. this work systematically identifies four critical obstacles: difficulties in physical integration due to device heterogeneity; constraints imposed by the limited computing power and storage resources of edge nodes on PQC algorithm execution; compatibility gaps between QKD/PQC and existing industrial protocols (such as Modbus and Profinet); and the engineering cost pressures associated with large-scale deployment. To address these issues, the paper proposes three lightweight countermeasures:(1) A tiered deployment architecture for quantum-safe security that accommodates device heterogeneity, enabling dynamic trade-offs between security strength and resource overhead; (2) middleware facilitating the synergy of quantum-classical hybrid network protocols, ensuring seamless integration between QKD/PQC and industrial control protocols; and (3) a distributed key management mechanism featuring three-level collaboration—edge node, network proxy, and central system—which offloads computation- and storage-intensive tasks to high-resource nodes, thereby alleviating bottlenecks at the edge. Experimental validation within a smart manufacturing factory's sensor network demonstrates that this scheme maintains quantum-safe capabilities while reducing per-node deployment costs by approximately 81.7% compared to pure QKD solutions and by about 32% compared to pure PQC solutions, all while facilitating seamless, non-disruptive upgrades. This study indicates that lightweight algorithm design, hardware-software co-optimization, and tiered architectural deployment constitute a viable pathway for advancing IoT edge security into the quantum-safe era.

Abstract: Quantum uncertainty and wavefunction collapse remain among the most conceptually unresolved aspects of microscopic physics. Here, we investigate whether uncertainty and collapse-like electron localization may admit a complementary thermodynamic interpretation through recursive entropy-field dynamics. The electron is modeled as a dynamically evolving entropy field–geometry composed of structural, electromagnetic, and thermal entropy components maintained through continuous recursive interaction with the surrounding vacuum entropy field. Within this framework, uncertainty emerges from incomplete temporal accessibility to rapidly evolving recursive electron configurations occurring beneath experimentally accessible timescales. Repeated recursive phase sampling naturally produces probabilistic measurement statistics and approximately Gaussian localization statistics. Electron–photon interaction is further modeled through phase-matched recursive entropy coupling, where repeated entropy transfer progressively reorganizes the electron entropy geometry toward localization. Numerical simulations reproduce finite-width Dirac-delta-like localization behavior, localization saturation after a finite number of recursive cycles, and intrinsically nonzero collapse timescales, with a lower bound of approximately 3.2×10⁻²⁰ s for an electron at rest under ideal recursive coupling. These results suggest that wavefunction collapse may emerge as a finite recursive thermodynamic localization process rather than an instantaneous state projection. The framework further provides a qualitative thermodynamic interpretation that relates normalized recursive accessibility to Born probability, discusses recursive entropy evolution as a possible physical basis for intrinsic quantum timescales, and outlines how Bell-type correlations may emerge within an extended recursive entropy framework. Together, these results establish a phenomenological foundation for recursive entropy dynamics underlying quantum uncertainty and localization while providing experimentally testable predictions and a systematic roadmap for future quantitative development.

Abstract: The increasing integration of communication networks, intelligent electronic devices, distributed energy resources, and automated control has transformed the electric grid into a tightly coupled cyber-physical system. This transformation also creates opportunities for false-data injection attacks (FDIAs), in which adversaries manipulate measurements or telemetry to bias state estimation and influence operational decisions. Coordinated FDIAs are especially challenging because several measurements, devices, or communication paths can be manipulated in a spatially and temporally consistent manner. A detector that examines individual measurements in isolation may therefore fail to identify an attack that appears plausible at each local point while being harmful at system level. This paper develops a cyber-physical resilience framework for coordinated FDIAs. The framework extends protection beyond attack detection by organizing defenses into preparation, observation, detection, localization, response, recovery, and adaptation. It combines physics-based state estimation with data-driven anomaly detection, network and device telemetry, adaptive measurement trust, and operational safeguards. The paper also proposes a reproducible simulation protocol for evaluating the framework on benchmark power-system models under different attack coverage, duration, grid-condition, and communication-failure scenarios. Rather than reporting unperformed experiments, the manuscript defines the metrics, baselines, ablation studies, and reporting standards required for a credible empirical evaluation. Finally, it discusses how recent work on adversarially robust photovoltaic diagnosis can inform hybrid detector design while distinguishing photovoltaic fault diagnosis from grid-level FDIA detection. The result is a structured research agenda for designing smart-grid defenses that preserve safe operation, not merely classification accuracy.

Abstract: Cultural tourism is usually identified from travellers’ primary motive, yet what visitors actually do at the destination may reveal a broader heritage market. Using a hypothetico-deductive design, this study examines 3,551 trip records from the March 2026 public-use file of Spain’s Encuesta de Turismo de Residentes (ETR/FAMILITUR), applying official survey weights. Cultural engagement is derived from reported cultural visits, performances, and other cultural activities. Among culturally engaged travellers, 56.4% did not report culture as their primary motive (bootstrap 95% CI: 52.5–60.5%), and agreement between motive- and engagement-based classifications was only moderate (κ = 0.459). Multiple correspondence analysis and weighted latent class analysis support a four-class solution (BIC = 36,486.13; entropy = 0.832). The Urban-Cultural Explorers class accounts for 21.2% of weighted trips, although only 46.1% of its members report a cultural primary motive. In adjusted models, cultural engagement is associated with approximately 14.0% higher total expenditure (p < 0.001), whereas stated cultural motivation is not significant. Engagement also performs better in information-criterion comparisons and repeated cross-validation. No significant satisfaction effect is detected; cultural engagement is associated with lower previous visitation, and hidden heritage tourists exhibit a distinct multivariate trip profile. Overall, the results reveal a measurable motivation–engagement gap with implications for heritage-market sizing, segmentation, and economic-value assessment.

Abstract: This study provides a unified theoretical and empirical examination of the generalized hyperbolic (GH) distribution family. The special and limiting relationships among the GH distribution and its important subclasses are reviewed. The empirical analysis considers daily returns on the SPDR SP 500 ETF Trust (SPY) from January 4, 2010, to July 22, 2024. The unrestricted GH distribution and its variance–gamma(VG), normal–inverse Gaussian (NIG), normal reciprocal inverse Gaussian (NRIG), hyperbolic (H), and HA subclasses are estimated by maximum likelihood method. Overall, the results demonstrate that the GH family provides a flexible and effective alternative to the Gaussian benchmark, although the preferred subclass depends on whether predictive adequacy or model parsimony is emphasized.

Abstract: Geothermal resource quality alone does not determine whether a project can be developed under favorable local and institutional conditions. This study integrates thermodynamic resource quality with site-level development feasibility for 18 Japanese geothermal sites included in a published Specific Exergy Index (SExI) dataset. Development feasibility is represented by a weighted score based on four criteria: hot-spring conflict risk, environmental and zoning constraints, consensus-building and regional acceptance, and infrastructure accessibility. SExI and development feasibility show a positive but statistically non-significant association (Pearson r = 0.430, p = 0.075). Using thresholds of 0.5 on both axes, 5 sites are classified as high SExI/high feasibility, 1 as high SExI/low feasibility, 6 as low SExI/high feasibility, and 6 as low SExI/low feasibility. Alternative weighting scenarios preserve the baseline quadrant membership, although sites near the feasibility threshold remain more sensitive to scoring assumptions. These results show that thermodynamic quality and development feasibility are related but distinct dimensions. The proposed matrix provides an exploratory screening framework for identifying whether geothermal development is primarily limited by resource quality or by site-specific socio-institutional conditions.

Abstract: Network intrusion detection requires accurate classification of high-volume network traffic while maintaining reliable evaluation and practical analyst support. This study presents a supervised network anomaly-classification workflow using the BigFlow-NIDS V2 benchmark, integrating leakage-aware data separation, SMOTE-Tomek balancing, FlowTransformer classification, confidence and novelty scoring, bounded Large Language Model (LLM) assistance, and analyst triage. The quantitative experiment uses a controlled binary subset of Benign and Scanning traffic, comprising 3,972 usable flows and 795 independent test observations. On the independent test set, FlowTransformer achieved 0.9107 accuracy, 0.9119 macro-precision, 0.9105 macro-recall, 0.9106 macro-F1, 0.9105 balanced accuracy, MCC of 0.8225, Cohen's kappa of 0.8213, and macro PR-AUC of 0.959. A RandomForest baseline achieved 0.9308 accuracy. Paired McNemar, Wilcoxon, and t-tests indicated a statistically significant difference, with the baseline performing better on this test set. The LLM was restricted to bounded advisory analysis, while analyst triage remained separate from model outputs. The results provide a reproducible binary evaluation and a basis for extending the workflow to the full multiclass benchmark.

Abstract: Atmospheric blocking is a major feature of mid-latitude circulation and is closely associ-ated with the occurrence of extreme weather events over Europe. Among the different blocking regimes, Omega blocks are characterized by their persistent tripolar structure, which favors the development of prolonged heatwaves. In late June 2026, Western Eu-rope experienced one of the earliest and most intense heatwaves of recent decades. This study investigates the synoptic, dynamical, and climatic mechanisms associated with this event using ERA5 reanalysis data. The evolution of the 500 hPa geopotential height, 850 hPa temperature, polar jet stream, Rossby wave breaking, and dynamical tropopause (2 PVU) was analyzed together with anomalies in sea surface temperature (SST), meridional temperature gradient, soil moisture, and sensible heat flux. The results show that the heatwave developed under a persistent Omega blocking pattern, which promoted strong subsidence, enhanced solar heating, and sustained warm air advection over Western Europe. The event was further amplified by land–atmosphere feedbacks associated with anomalously dry soils. In addition, a pronounced cold SST anomaly over the subpolar North Atlantic preceded the onset of the blocking and coincided with a weakened me-ridional temperature gradient and reduced lower-tropospheric baroclinicity along the climatological position of the polar jet stream. These findings are consistent with recent studies suggesting that North Atlantic cooling associated with a weakened Atlantic Me-ridional Overturning Circulation (AMOC) may create favorable conditions for persistent summer Omega blocking and associated European heatwaves.

Abstract:

Azole resistance in Aspergillus fumigatus is an emerging threat to invasive and chronic aspergillosis management, yet multicenter trend analyses rarely verify whether the composition of surveillance data remained stable over time. We performed a longitudinal, secondary-data analysis of 2,111 Aspergillus isolates registered in the VIVLI AMR platform (ATLAS Antifungals, SENTRY Program) between 2010 and 2020. Because Clinical and Laboratory Standards Institute (CLSI) voriconazole breakpoints are established only for A. fumigatus in this dataset, the susceptibility-trend analysis was restricted to A. fumigatus isolates with a valid interpretation (n = 1,482/1,483). A. fumigatus accounted for 70.3% of all Aspergillus isolates; most originated from Europe (43.9%) and North America (35.2%). Region and clinical specialty differed significantly across the three study periods (both p < 0.0001), while species, age, sex and specimen-source distributions remained stable (p > 0.10). Voriconazole susceptibility in A. fumigatus declined from 97.9% (2010) to 90.4% (2020), with a significant linear downward trend (Cochran-Armitage Z = −3.03, p = 0.002). This decline was statistically robust, but the concurrent shift in participating regions and specialties indicates that compositional changes in surveillance sampling should be considered alongside biological resistance emergence when interpreting multicenter antifungal surveillance trends.

Abstract: Let (X,τ) be a topological space and let τ_h(X) denote the topology formed by the h-open subsets of X. We develop h-density, h-separability and countable h-dense homogeneity systematically through the associated space (X, τ_h(X)). We prove that h-density and h-separability are precisely ordinary density and separability in the associated topology, and that h homeomorphisms are exactly homeomorphisms between associated spaces. Consequently, (X,τ) is h-countable dense homogeneous (h-CDH) if and only if (X, τ_h(X)) is countable dense homogeneous (CDH). A classical theorem on CDH spaces then yields that every h-CDH space is h-T₁. Hence its h-specialization preorder is equality, hCl({x}) = hKer({x}) = {x} for every point, and the pointwise closure–kernel defect measured by the h-RT condition vanishes identically. We give explicit examples showing that or dinary CDH and h-CDH are incomparable in general, while they coincide in Hausdorff spaces. We formulate a product-transfer principle for arbitrary families under compatibility of associated and product topologies and show, using a metrizable counterexample, that compatibility alone does not make h-CDH productive. Finally, we relate the theory to h-normality, including an infinite-product transfer criterion, and formulate several questions concerning normality, product compatibility and converses to the h-RT consequence.

Abstract: We introduce and investigate forms of regularity and normality defined by means of h-open sets. Using the associated topology τ_h = hO(X), we interpret h-regularity as a relative regularity property in which points and closed sets of the original topology are separated by τ_h-open sets, whereas strong h-regularity is precisely the ordinary regularity of (X, τ_h). We establish characterizations of h-regularity, study its preservation under suitable mappings, compare the new notions with their classical counterparts, and examine related separation properties.

Abstract: Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic regulation, suitable for use in monitoring training adaptation and recovery in athletes. Group-level analyses may mask significant interindividual variability in responses to the same training stimulus if changes are divergent. This study investigated whether competitive wrestlers exposed to a standardized training protocol exhibit different multivariate patterns of cardiac autonomic response. Their evolution during a four-month training period was also investigated. Holter recordings were obtained before and immediately after training, with assessments performed at five measurement points. Temporal, frequency, nonlinear, and fractal parameters of HRV were analyzed. Individual percent changes before and after training were assessed using inferential and correlation analysis, principal component analysis (PCA), and hierarchical clustering. Two distinct physiological response profiles were identified. The predominant profile is characterized by increased heart rate and decreases in RMSSD, SDNN, SampEn, and other vagally mediated HRV indices. A smaller subgroup shows the opposite response pattern with preserved or increased variability. The profiles are differentiated by changes in RMSSD, SDNN, and LF/HF. The first two principal components explain 80.53% of the total variance. Hierarchical clustering and cluster quality indicators support multivariate separation. Longitudinal assessment shows that contrasting profile-specific directions of response remain distinguishable during the four-month training period. The magnitude of several acute HRV responses is observed to change over time. Identical training stimuli can elicit different cardiac autonomic responses among wrestlers. The study supports longitudinal HRV profiling as a complementary approach for individualized athlete monitoring and training response assessment.

Abstract: Background: Tumour-growth kinetics required to diagnose hyperprogressive disease are often unavailable in routine care. We developed baseline and dynamic clinical–laboratory models for progression or death within 90 days (EPD90), a pragmatic endpoint for early clinical deterioration, and assessed whether six-week laboratory change improved risk estimation. Methods: This retrospective single-centre cohort included consecutive adults with advanced or unresectable non-small cell lung cancer treated with PD-1/PD-L1 blockade between January 2018 and March 2026. EPS-4 combined bone metastasis, lactate dehydrogenase (LDH) >240 U/L, albumin < 3.5 g/dL and C-reactive protein >10 mg/L. Day-42 landmark models added log2 ratios of neutrophil-to-lymphocyte ratio (NLR) and LDH. Performance was assessed with 2000 bootstrap resamples and repeated cross-validation. Results: Among 282 assessable patients, 83 (29.4%) had EPD90. In common complete cases (n = 252; 76 events), EPS-4 had an AUC of 0.729 (95% CI 0.665–0.793). Among 238 patients alive and progression-free at day 42 (56 subsequent events), AUC increased from 0.718 for EPS-4 alone to 0.801 after adding NLR change and 0.816 after adding LDH change. The final increment from LDH was modest (ΔAUC 0.016; P = 0.094). Conclusions: Baseline EPS-4 and early NLR change identified higher-risk patients, but the models remain development-stage and require independent prospective validation.

Abstract: In post-prostatectomy prostate cancer, combining androgen deprivation therapy (ADT) with salvage radiotherapy (SRT) significantly improves clinical outcomes, though the absolute benefit varies based on baseline PSA, pathological risk, and treatment design. This review synthesizes findings from key randomized trials and contemporary meta-analyses including RTOG 9601, SPPORT, GETUG-AFU 16, GETUG-AFU 22, and RADICALS-HD evaluating the integration and duration of ADT with postoperative irradiation. While systemic therapy consistently enhances biochemical and clinical progression-free survival, a universal overall survival benefit remains uncertain or limited to high-risk cohorts with elevated pre-treatment PSA values. Concurrently, data from the prospective phase II trials OLIGOPELVIS (GETUG P07) and PEACE V - STORM are redefining the standard of care for pelvic nodal oligorecurrent disease detected via next-generation imaging (NGI). Transitioning away from exclusive lifelong ADT or isolated focal metastasis-directed therapy, emerging evidence strongly supports a multimodal paradigm: combining prostate bed RT, whole-pelvis elective nodal irradiation, and a high-dose boost to PET-positive nodes, complemented by a short course of systemic therapy. This narrative review provides readers with effective indication to use adjuvant HT according recent literature evidences to maximizes regional disease control and delays systemic progression, while successfully sparing the majority of patients from the toxicities of continuous androgen deprivation.