Search Results (291)

Background/Objectives: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for several advanced malignancies, but their use is accompanied by immune-related adverse events, including liver injury. Some cases resemble autoimmune hepatitis (AIH), although many are more accurately described as AIH-like immune-mediated hepatitis rather than classical AIH. This distinction matters, as diagnosis is often based on exclusion and management must balance hepatic recovery against interruption of potentially life-prolonging cancer therapy. This systematic review summarised the clinical phenotype, diagnostic assessment, treatment strategies, treatment response, ICI discontinuation, and rechallenge outcomes in patients with ICI-associated AIH-like liver injury. Methods: A systematic PubMed search was performed for English-language human studies reporting autoimmune hepatitis, AIH-like liver injury, or immune-mediated hepatitis following exposure to ICIs. Eligible studies included case reports, case series, retrospective cohorts, prospective cohorts, and pharmacovigilance-type studies with extractable clinical, treatment, or outcome data. Reviews, guidelines, non-original articles, animal studies, non-English publications, and reports without usable liver injury data were excluded. The review followed PRISMA principles. Risk of bias was assessed using Joanna Briggs Institute tools and summarised with ROBVIS. Given the heterogeneity of study design, diagnostic criteria, treatment definitions, and outcome reporting, formal meta-analysis was not appropriate; results were therefore synthesised descriptively. Results: Twenty-two studies were included, comprising 195 patients with ICI-associated AIH-like or immune-mediated hepatitis. Of these, 140 patients received active treatment, and 133/140 achieved clinical or biochemical recovery with varying therapies. Corticosteroids were the most frequently used first-line therapy, with recovery reported in 102/105 patients treated with corticosteroids alone. Mycophenolate mofetil was the main second-line agent for steroid-refractory disease, with response reported in 9/10 treated patients. Other therapies, including tacrolimus, azathioprine, ursodeoxycholic acid, bezafibrate, tocilizumab, basiliximab, infliximab, budesonide, and double plasma molecular adsorption system with or without plasma exchange, were described only in small numbers or isolated cases. Spontaneous recovery without pharmacological treatment was reported in 19 patients. ICI interruption or discontinuation occurred in 141 patients, and rechallenge was reported in 55 patients after recovery, with no recurrent hepatic toxicity documented in the extracted dataset. Conclusions: ICI-associated AIH-like liver injury is an important immune-related toxicity, but the available literature remains fragmented and methodologically heterogeneous. Most reported patients recovered, particularly with corticosteroids, and MMF appears to be the most consistently used escalation therapy in steroid-refractory cases. However, the strength of evidence is limited by uncontrolled designs, variable terminology, inconsistent diagnostic work-up, and non-standardised outcome definitions. Future studies should separate classical AIH from AIH-like immune-mediated hepatitis, use uniform criteria for severity and response, and report treatment denominators clearly, especially for rechallenge and steroid-refractory disease. Full article

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m⁻²) than for Hs (±5 W∙m⁻²). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m⁻²), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m⁻²) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m⁻² for Hs and <7 W∙m⁻² for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m⁻² at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction. Full article

It is well established in social psychology that intergroup contact is beneficial to reduce intergroup bias. Based on this insight, a growing body of work has focused on correlates of people’s motivation to seek or avoid intergroup contact. The present study contributes to this literature by probing motivation for an ecologically valid and ideal form of intergroup contact: intercultural twinnings (structured exchange activities between people of diverse linguistic and ethnocultural backgrounds). We examined three facets of motivation (contact willingness; intrinsic motivation; and contact opt-in as a proxy for behavioral intent) and a range of well-established intergroup contact associates. We also tested the role of cultural congruity with the dominant society, inspired by push–pull theories of migration. Participants included 214 students in Québec, Canada. The results show that motivational profiles differed depending on motivation facets. Intergroup anxiety was negatively related to intrinsic motivation; desire for self-expansion was positively related to all three facets; ethnocentrism was negatively associated with our behavioral proxy. Cultural congruity was associated with all three facets through a suppression effect, such that greater perception of not fitting in Québec society was related to higher motivation indices once avoidance dispositions were taken into account. With prevalent intergroup tensions, better understanding how to “bring the horse to the contact water” is essential. Full article

Neurons achieve their highly polarized architecture by coordinating cytoskeletal systems across space and time, enabling axons to extend over remarkable distances and dendrites to elaborate complex arbours. Early neuroanatomists described intracellular “neurofibrils,” yet these structures remained poorly understood until electron microscopy resolved them into three distinct polymer systems: microtubules, actin filaments, and intermediate filaments. Although this framework clarified neuronal ultrastructure, it simultaneously established a conceptual hierarchy in which microtubules and actin were regarded as the principal drivers of neurite growth, while intermediate filaments were relegated to a passive, supportive role. Unlike prior reviews that document vimentin dynamics primarily from a cell-biological standpoint, this review integrates historical, conceptual, and epistemological perspectives to examine both how and why that hierarchy arose and how it has been dismantled. This review traces how that hierarchy arose and why it has been increasingly reconsidered in favour of intermediate filaments, focusing on vimentin as a case study. Evidence from live cell imaging, molecular manipulation, and genetic models shows that vimentin is dynamically regulated rather than static. Vimentin networks remodel continuously, exchange subunits with soluble pools, and move in coordination with microtubules. Most recently, sparse single-filament labelling combined with correlative volume electron microscopy has demonstrated that individual vimentin filaments remain motile even within dense perinuclear networks previously assumed to be static, a finding that fundamentally redefines what filament density implies about cytoskeletal organization. In neural and neural precursor cells, vimentin expression is developmentally regulated and is prominent during early differentiation stages associated with neurite initiation giving way to neurofilaments in mature neurons. Functional studies further link vimentin to neurite formation and extension, cytoskeletal coordination, organelle positioning, and cellular stress responses. Philosophical analysis reveals that these empirical advances were inseparable from shifts in imaging technology and conceptual framing, and that epistemic risks including model dependency and confirmation bias can be mitigated through methodological pluralism and explicit model disclosure. Taken together, these findings support a revised understanding of intermediate filaments as active, context-dependent contributors to neuronal development and plasticity, and illustrate the value of integrating biological evidence with historical and philosophical reflection. Full article

Underwater image analysis is affected by light scattering, wavelength-dependent attenuation, low contrast, and suspended particles, which reduce the discriminative visual features. Current multi-scale Vision Transformers are not well-suited to these degradations because they cannot effectively fuse features across scales to achieve accurate classification. Although Vision Transformers (ViTs) can model long-range interactions, single-scale patch tokenization remains suboptimal for underwater images, where both fine-grained textures and global structures are important. This study proposes a Multi-Scale Vision Transformer (MS-ViT) with Cross-Scale Biased Attention Fusion (CSBAF) for underwater image classification. Before transformer encoding, the CSBAF introduces a learnable source–target scale-pair bias and an input-dependent scale-reliability gate. This differs from standard multi-scale fusion and cross-attention methods, which mainly concatenate features or exchange information between scale branches. The proposed design enables the model to emphasize reliable scales while suppressing degraded-scale responses. A hybrid dataset containing 14,000 images from the Roboflow Aquarium and RUIE datasets across five classes was used for evaluation. MS-ViT with CSBAF achieved 88.9% accuracy and an 88.8% F1-score, outperforming the CNN baseline by 7.6% and state-of-the-art transformer models, including UWFormer, DP-ViT, and CvT, by 2.3–4.2%. Ablation studies showed a 1.7% accuracy improvement over simple multi-scale concatenation, whereas cross-dataset testing achieved 84.4% accuracy, indicating reasonable cross-dataset robustness. These results demonstrate that explicit scale–aware fusion can improve transformer-based underwater visual understanding. Full article

This exploratory study aimed to compare the determination of metabolic thresholds using muscle oxygen saturation (SmO₂) and gas exchange data during graded exercise testing in individuals with spinal cord injury. Nine participants (six males, three females) performed a graded exercise test on an arm-crank ergometer, with continuous measurements of breath-by-breath pulmonary gas exchange and SmO₂ from the biceps brachii and triceps brachii. Thresholds were identified as gas exchange threshold (GET) and respiratory compensation point (RCP), and their SmO₂ counterparts as MOT1 and MOT2. The results showed no differences between GET and MOT1 in either muscle (biceps: p = 0.14; triceps: p = 1.00), and similar results were observed between RCP and MOT2 for triceps brachii (p = 0.39) and biceps brachii (p = 0.12). Reliability analysis revealed good but non-significant agreement for the triceps brachii (ICC = 0.44–0.60), while the biceps brachii demonstrated very good agreement at GET (ICC = 0.78, p < 0.01) and excellent agreement at RCP (ICC = 0.81, p < 0.01), and Bland–Altman analyses confirmed no systematic bias between muscle sites. In conclusion, SmO₂ may be a valid and promising variable for detection in individuals with spinal cord injury during ramp exercise testing. Both muscles showed agreement with pulmonary gas exchange, but the biceps brachii provided more consistent and reliable estimates, particularly for the second threshold. These preliminary findings suggest the use of near-infrared spectroscopy as a non-invasive technology for metabolic threshold detection in spinal cord injury populations. Full article

This study examines the association between foreign exchange (FX) governance and financial stability by analysing empirical evidence from multinational entities. We analyse a 16-year panel (2009–2024) comprising 6613 firm-year observations using OLS regression with industry and year fixed effects. Firm-level data on financial sustainability, FX governance, board attributes, and controls are drawn from the London Stock Exchange Group (formerly Refinitiv), while country-level institutional and economic indicators are obtained from the World Bank. The result suggests that FX governance is negatively associated with earnings volatility, implying that FX governance enhances the financial stability of organisations. The baseline result is robustness to endogeneity and selection bias. However, our subsample analysis reveals that the impact of FX governance on financial stability varies based on institutional quality and industry. Whereas FX governance is negatively associated with earnings volatility thus enhancing financial stability in high-institutional-quality settings, the impact is not significant in low-institutional-quality environments. This study contributes to knowledge by empirically validating the relevance of FX governance to financial stability. Our study also contributes to the limited studies on the role of FX governance in diminishing earnings volatility, thus exposing FX management as a strategy for achieving financial sustainability. The international sample analysed in the study contributes to the generalisability of results. Full article

Soil micro- and nanoplastic contamination is escalating globally, yet its potential to interfere with routine agrochemical analyses remains poorly quantified. Standard operating procedures (SOPs) were calibrated for natural soil matrices and may not account for synthetic, carbon-rich polymers. This controlled model study quantified the analytical sensitivity of FAO/GLOSOLAN/ISO standard procedures to polystyrene nanoparticle (50 nm) contamination across a 0–0.5% (w/w) gradient in a Luvic Chernozem. Key parameters—pH, soil carbon, total nitrogen (TN), cation exchange capacity (CEC), and clay fraction—were measured following standardized protocols. The Walkley–Black method exhibited a strong dose-dependent increase in measured SOC (r = 0.93), reflecting systematic overestimation due to dichromate co-oxidation of polymer matrix, likely facilitated by exothermic heating above polystyrene’s glass transition temperature. The Dumas method showed moderate correlation (r = 0.59) but higher replicate variability driven by small aliquot size and heterogeneous nanoparticle distribution. The pH measurements displayed non-linear responses and elevated variability at low doses, whereas TN, CEC, and clay content remained statistically stable. These findings demonstrate that nanoplastic contamination can introduce significant analytical artifacts in oxidation-based SOC determinations, potentially leading to misinterpretation of soil carbon trends. Given the single-soil, single-polymer design, results represent a system-specific proof of analytical vulnerability rather than a universally quantified bias. Laboratories analyzing potentially contaminated soils should exercise caution with wet-oxidation SOC data, and broader SOP revisions must await multi-soil, multi-polymer validation campaigns. Full article

Accurate price forecasting and portfolio optimization in emerging financial markets remain challenging due to the non-stationary dynamics, structural breaks, and heterogeneous behavior of traded instruments. This paper proposes the Time-series Adaptive Forecast Ensemble (TAFE), a method that combines single popular forecasting methods, such as ARIMA, by using an algorithm derived from both the simulated annealing (SA) and Threshold Accepting algorithms. The TAFE is applied to twenty-four weekly price series of Mexican exchange-traded funds (ETFs) and Real Estate Investment Trusts (FIBRAs) over the period 2020–2025. A top-K pre-selection strategy is used, mitigating the adverse cross-model interaction effect of some assets over others, in other words, reducing the propagation of errors from poorly performing base learners. In addition, the sample results show that the TAFE achieves the lowest mean SMAPE across the panel, with statistical superiority over the equal-weight benchmark and a Hybrid Model, confirmed by Diebold–Mariano and Harvey–Leybourne–Newbold tests. Out-of-sample evaluation over a 26-week horizon reveals a regime-shift-driven performance reversal consistent with the bias–variance tradeoff in adaptive combination schemes. Portfolio optimization using SA-generated forecasts yields with an expected return of 35.77%; thus, the model presents a slight overestimation of the return, with a variance of 2.4%. However, it has an acceptable level of risk. Full article

Large-scale shell-and-tube heat exchangers operate for extended periods, critically affecting the energy efficiency and safety of hydrogen production processes. However, online condition monitoring on industrial distributed control systems (DCSs) is often hindered by an engineering trilemma: high-fidelity mechanistic models incur prohibitive computational latency; static constant-parameter models suffer from severe systematic bias; and purely data-driven models risk yielding non-physical predictions under out-of-distribution scenarios such as variable-load operations. To address these challenges, this study proposes a physics-guided adaptive digital twin tailored to high-noise industrial DCS environments. Energy conservation and the counterflow logarithmic mean temperature difference (LMTD) relation are embedded as hard constraints in a lightweight reduced-order model (ROM). On this basis, a closed-loop online adaptation strategy—comprising physical-bound checking, window-wise inverse estimation, anomaly rollback, and exponentially weighted moving average (EWMA) smoothing—treats the overall heat transfer coefficient U as an equivalent time-varying parameter that co-evolves with operating regimes. Validation on real plant DCS data under variable-load conditions shows that, compared with a conservative fixed-U baseline, the proposed online update eliminates massive systematic overestimations (up to tens of degrees Celsius) and suppresses inversion oscillations caused by small cold-side temperature differences and sensor noise. Relative to an overfitting-prone data-driven baseline, the framework retains millisecond-level inference latency while enforcing thermodynamic feasibility, thereby establishing a dynamic healthy baseline. This baseline provides a proxy indicator for distinguishing load-induced reversible variations from potential degradation-related residual trends. Full article

In terms of rollout, comprehensiveness, and strategy, Singapore’s regulatory landscape governing the ethical use of Artificial Intelligence (AI) in healthcare has generally kept pace with other global leaders in AI advancement. However, establishing a robust and holistic regulatory framework that evolves along with emerging technologies is not easy—especially in healthcare, where the stakes are high and resources may be limited. We conducted a structured scoping analysis of key AI regulatory and professional documents in Singapore, selected using predefined inclusion criteria. Documents were systematically mapped against Savulescu et al.’s nine categories of ethical risk, followed by cross-document comparison to identify integration gaps and inconsistencies, and benchmarking against international AI governance frameworks. These recommendations are generalizable beyond Singapore for developers, implementers, healthcare professionals and patients and include dealing with bias in AI, enhancing human productivity without deskilling, facilitating more informed decision-making, and cultivating greater knowledge exchange between clinicians and patients, to name a few. Full article

The Kimchi Premium—the persistent price wedge between Bitcoin on Korean and global exchanges—has resisted standard no-arbitrage explanations for over seven years, raising the question of how macro-financial shocks transmit into this segmented market. Prior work relies on static, linear estimators applied over short lag horizons, leaving the timing, nonlinearity, and regime dependence of the Premium’s adjustment largely untested. We address this gap using daily data from December 2017 to December 2025 ($T=2105$) and rolling windows of 60, 120, and 240 trading days. Linear dynamics are tested with Granger causality (GC) and—because our return series are strongly leptokurtic (Bitcoin excess kurtosis $=7.29$, S&P 500 $=14.77$)—complemented by the Kraskov k-nearest-neighbor transfer entropy (TE) estimator, which captures conditional dependence in higher moments. Inference rests on a stationary bootstrap with pre-specified lag grids to avoid optimistic argmax bias, block-permutation tests for window-level detection rates, and Benjamini–Hochberg and Storey q-value corrections for multiple testing. Robustness is examined through conditional GC and conditional TE controlling for USD/KRW as a common factor, Toda–Yamamoto tests on price levels, a percentage-premium specification, a U.S. trading-day shift to address asynchrony, and winsorization sensitivity. We deliberately adopt a conservative inference stance at the panel level: window-level detection rates and pointwise bootstrap p-values are reported, but claims of “causality” are reserved for the within-window descriptive ranking of channels and horizons, with the panel-level null assessed by block-permutation and Benjamini–Hochberg corrections. Four empirical patterns emerge under this framing. First, Johansen tests identify a single cointegrating vector between Korean and global Bitcoin prices (trace $=91.99$ vs. 5% critical value $15.49$), establishing the Premium as a stationary deviation from long-run parity, while none of the four macro indicators cointegrate with global Bitcoin. Second, GC detection rates for the Premium concentrate at the 240-day horizon: Gold → Premium reaches $23.3\%$ (95% Wilson CI $[19.3\%,27.8\%]$), KOSPI 200 → Premium $16.3\%$, and USD/KRW → Premium $16.8\%$. Third, the Kraskov TE reveals an asymmetry invisible to linear tests: for Gold → Premium at $w=240$, the median optimal lag is five days, against one day for Gold → Bitcoin (chi-square $p=0.017$). Percentage-premium detection rates are substantially higher (e.g., $59.1\%$ for Gold → Premium at $w=240$), indicating that the dollar-wedge specification understates causal strength. Fourth, block-permutation tests do not reject the global null of no window-level excess rejection, and Benjamini–Hochberg rejects no pair at $\alpha =0.05$; we therefore read the detection-rate evidence as descriptive of localized, crisis-dependent transmission episodes rather than as panel-level rejection of pointwise non-causality, and the paper’s contribution is accordingly positioned at the level of channel ranking, lag structure, and regime decomposition rather than at the level of blanket causality claims. Crisis decomposition shows transmission is concentrated in the ETF-Halving regime ($29.8\%$ mean GC detection) and below $4\%$ during the Terra–Luna ($2.9\%$) and Russia–Ukraine ($3.6\%$) episodes. The findings situate the Premium as a stationary error-correction term whose adjustment is dominated by exchange-rate and commodity channels rather than U.S. equities, with implications for arbitrage models, regulatory monitoring, and information-flow analyses of segmented crypto markets. Full article

Ocean surface currents (OSCs) are central to upper ocean dynamics and air–sea exchange, yet their retrieval from spaceborne synthetic aperture radar (SAR) is limited by wave-induced bias (WB). WB arises from the inherent motion of the scattering facets and from long-wave hydrodynamic and tilt modulations, and is therefore jointly controlled by sea state and radar viewing geometry. This study develops an observation geometry optimization framework. Five years of hourly ERA5 wind and wave reanalysis data over the Kuroshio are used as a representative ensemble of sea states to drive the KaDOP model, and an exhaustive grid search over line-of-sight (LOS) azimuth (0–360°) and incidence angle (20–60°) is performed to identify, for each location and season, the viewing geometry that minimizes the time-mean WB. These local optima are then summarized as mission-level metrics, including the minimum achievable WB, the coverage meeting prescribed WB thresholds, and the spatial coherence of the preferred LOS azimuth and incidence angle. Finally, the theoretical minima are compared with the fixed left-looking geometry of the Luojia-2 (LJ-2) satellite along a 213 km × 6 km observation corridor and with Gaofen-3 (GF-3) viewing geometries at four representative locations in the Kuroshio. Across these validation cases, the optimized geometry reduces mean absolute WB by about 20–60% for LJ-2 and 20–80% for GF-3, providing quantitative constraints for future SAR mission design targeting OSCs. Full article

Rapid urbanisation and climate-induced extreme weather events have intensified urban stormwater runoff challenges. Biofiltration systems have emerged as effective, sustainable urban drainage solutions for mitigating these impacts. A total of 78 peer-reviewed studies were assessed to synthesise findings on how design parameters influence pollutant removal performance in biofiltration systems treating urban stormwater runoff. Peer-reviewed articles published from 1 January 1995 to 3 June 2025 were retrieved from Scopus and Web of Science (WoS). Non-peer-reviewed, non-empirical, incomplete, or non-relevant studies were excluded. Rigorous application of a standardised review protocol and predefined criteria was employed to mitigate bias. The findings reveal high removal efficiencies for certain trace metals, ammonium, Escherichia coli (E. coli), hydrocarbons, and microplastics, with inconsistent removal for total nitrogen, nitrates, and phosphorus. The primary pollutant removal mechanisms were adsorption, ion exchange with select media, and denitrification in saturated zones. Only 22% of the reviewed studies incorporated a saturated zone, while 18% included a protective surface layer, despite both design elements being associated with improved pollutant removal performance. Variations in media composition and stormwater quality limit comparability across studies. This review highlights the need for context-specific design guidance and further exploration of multi-functional media to enhance multi-pollutant removal. Full article

The ongoing substance use crisis in the United States involves a broad range of illicit and prescription drugs, including opioids, stimulants, sedatives, and various psychoactive and non-psychoactive compounds. Traditional surveillance methods rely on self-reported data, which could lead to bias and recall inconsistencies. Wastewater-based epidemiology has emerged as a powerful, non-invasive tool for monitoring community-level drug use, offering near real-time estimates and the potential to serve as an early warning system. However, challenges such as analyte degradation, wastewater variability, and matrix effects can affect data quality and comparability across regions. This study presents a standardized, practical workflow for multi-drug (n = 52) detection in wastewater, aiming to minimize analyte loss and improve reproducibility. Composite samples were collected from multiple U.S. cities, transported on ice, and extracted using solid-phase extraction. Extraction efficiencies were compared using Oasis Hydrophilic-Lipophilic-Balanced and Mixed-mode Cation-Exchange (MCX) cartridges, with the MCX sorbent providing complementary reversed-phase and cation-exchange interactions that enabled the retention of chemically diverse compounds across multiple drug classes. Analysis was performed with an Ultra-High-Performance Liquid Chromatography system coupled to a triple quadrupole mass spectrometer, in which the instrument parameters and critical methodological considerations, including sample handling, transport, column selection, and method validation, are detailed. This work contributes to the development of a robust, scalable protocol for multi-drug surveillance in wastewater, supporting timely, data-driven public health responses and informing national drug policy efforts. Full article