Abstract: The mainstream ESG literature associates favorable board characteristics with improved corporate environmental performance, yet the gap between sustainability governance and actual environmental outcomes remains persistent and poorly explained. This paper develops a theoretical framework to account for that gap by introducing two pathological institutional logics that governance reform cannot correct and systematically worsens. The first is morocracy: governance by institutional incompetence, sustained through loyalty-based selection and patronal mechanisms. The second is algorithmic capture: the colonization of fiduciary judgment by AI-driven optimization systems constitutively blind to environmental values resisting monetization. Drawing on institutional theory, critical governance scholarship, and the board-characteristics literature, we argue that their combination produces a dual governance deficit whose most dangerous feature is not organizational paralysis but the expert performance of sustainability commitment by institutions structurally incapable of delivering it. Under these conditions, governance improvement does not close the performance-commitment gap. It compounds it, furnishing pathological institutions with increasingly credible instruments for sustainability theater. Against this diagnosis, we propose deliberative governance as the corrective institutional architecture, grounded in epistemic integrity, algorithmic subsidiarity, and environmental accountability. Five counterintuitive propositions are advanced to anchor the theoretical contribution and orient future empirical inquiry.

Abstract: Although Homo sapiens remains the only species with clearly documented religious systems, archaeological evidence suggests that other archaic humans may have exhibited behaviors associated with proto-religious thought, including symbolic practices and possible mortuary rituals. In particular, Homo neanderthalensis and the population known as Denisovans possessed large and complex brains, including well-developed frontal regions associated with social cognition and symbolic processing. This article explores the possibility that these archaic humans possessed early conceptualizations of death and mortality that could represent precursors to later religious ideas. By examining archaeological evidence, genetic research on archaic introgression, and theories from evolutionary anthropology and cognitive science of religion, the study investigates how interactions between archaic humans and modern humans may have contributed to the cognitive and cultural foundations of religious thought. Particular attention is given to the potential influence of archaic genetic heritage on cognitive traits related to agency detection, social cohesion, and attitudes toward death. The article also discusses whether such evolutionary and cognitive influences may have indirectly shaped later religious traditions in different cultural contexts, including both Western and Eastern religious systems. While direct causal connections remain difficult to establish, this study aims to provide an interdisciplinary framework for understanding how archaic human populations may have contributed to the deep evolutionary roots of human religiosity.

Abstract: We propose a structural framework for organizing the submanifold content of compact Calabi--Yau manifolds through the notion of a {Topological Slice Structure} (TSS), a coherent collection of calibrated submanifolds compatible with the Ricci-flat metric data. The central result is a decomposition principle asserting that, under mild conditions on the K\"ahler polarization, such a structure exists, its cohomology classes span the full integer homology, and it is covariant with respect to mirror symmetry. Special cases recover special Lagrangian torus fibrations, divisors, and holomorphic curves as natural constituents of a unified geometric datum. We illustrate the framework through worked examples, introduce a numerical slice complexity invariant, and discuss implications for D-brane wrapping and moduli stabilization in string compactifications.

Abstract: Metastatic colorectal cancer (mCRC) remains one of the leading causes of cancer-related mortality worldwide despite substantial therapeutic improvements over the past two decades. Advances in the understanding of colorectal tumor biology and oncogenic signaling, have enabled the development of biomarker-guided therapies targeting alterations in EGFR, BRAFV600E, KRAS mutations and HER2 amplifications, improving outcomes in selected patient populations. Nevertheless, the emergence of both intrinsic and acquired resistance mechanisms continue to limit the durability of these responses. Resistance to targeted therapies in mCRC arises through multiple, often convergent mechanisms, including activation of compensatory signaling pathways, pre-existing genomic heterogeneity, and therapy-driven clonal selection. The integration of molecular profiling into clinical decision-making is essential to guide treatment selection and op-timize therapeutic sequencing, ultimately enabling progress in precision oncology. Advances in genomic technologies, particularly circulating tumor DNA (ctDNA) analysis, have allowed longitudinal monitoring of tumor evolution, providing important insights into the mechanisms underlying resistance to targeted therapies. The aim of this review is to summarize the genomic landscape of mCRC and discuss current targeted therapeutic strategies in molecularly defined subgroups, with a particular focus on the mechanisms driving primary and acquired resistance.

Abstract: Urban air pollution remains a critical challenge in rapidly urbanizing metropolitan re-gions, where complex topography and uneven monitoring infrastructure limit accurate exposure assessment. Nitrogen dioxide (NO₂), primarily emitted from traffic and combus-tion sources, exhibits marked spatial heterogeneity that is often underrepresented by sparse ground-based stations. This study examines the spatiotemporal variability of tropospheric NO₂ over Ankara Province, Türkiye, for 2025 and develops and implements a machine learning-based downscaling framework integrating Sentinel-5P TROPOMI ob-servations with Sentinel-2 multispectral surface reflectance data, without relying on an-cillary meteorological or emission datasets. After rigorous quality filtering and temporal aggregation, a Random Forest regression model was used to generate annual NO₂ maps at 500 m resolution based solely on spectral predictors. Results indicate strong seasonal variability, with winter monthly means reaching 8.93 × 10⁻⁵ mol/m² and peak values ex-ceeding 30 × 10⁻⁵ mol/m², alongside a persistent urban–rural gradient radiating from the metropolitan core. The optimized model achieved consistent predictive performance (R² = 0.30; RMSE = 2.72 × 10⁻⁵ mol/m²), with SWIR and Red Edge bands contributing most strongly. These findings demonstrate that high-resolution urban NO₂ patterns can be re-liably inferred from optical satellite data alone, providing a transferable and scalable framework for air quality assessment in data-limited metropolitan environments.

Abstract: Population ageing is creating increasing demand for residential environments that support safety, independence, and well-being for older adults. However, existing design guidelines remain fragmented and often lack measurable spatial indicators applicable in architectural evaluation. This study proposes the Elderly Residential Environment Evaluation Matrix (EREEM), an integrated framework based on six environmental design principles: safety, accessibility, autonomy, privacy, social interaction, and adaptability. An expert survey involving 36 specialists was conducted to evaluate an initial set of 54 spatial indicators, showing high reliability (Cronbach’s α = 0.978). The indicators were subsequently refined into 24 operational indicators and applied in field assessments of four residential environments in Vietnam. The results confirm the reliability and applicability of the EREEM framework, highlighting safety and accessibility as foundational conditions for age-friendly residential environments. The study provides a systematic evaluation tool bridging environmental gerontology and architectural design, supporting sustainable and age-friendly residential development in ageing societies.

Abstract: Exercise-derived reactive oxygen species (ROS) are required for mitochondrial and hypertrophic adaptations, creating a practical trade-off: antioxidant strategies may support short-term performance and recovery yet blunt training signals when mis-timed or over-dosed. We performed a structured narrative review informed by transparent database searches of MEDLINE, Scopus, and SPORTDiscus (2000-2025), prioritizing human intervention studies and using mechanistic evidence to interpret plausibility. Evidence was mapped by antioxidant class, dose, timing, training modality, and context. Across trials, chronic high-dose vitamins C/E taken close to key sessions are most consistently associated with attenuation of redox-sensitive signaling, whereas food-first polyphenols and selected bioactives (e.g., tart cherry/anthocyanins, pomegranate, curcumin) more often support recovery when positioned away from adaptation-critical workouts, without clear evidence of impaired training gains. N-acetylcysteine can acutely improve tolerance to repeated high-intensity exercise, but effects during prolonged training remain uncertain and appear context-dependent. We propose Redox-Adaptive Periodization, aligning antioxidant class, dose, and timing with the primary objective (adaptation vs. immediate readiness) and environmental constraints, and we outline methodological priorities to advance precision redox management.

Abstract: Not all web tasks are feasible under strict cost and safety requirements, yet standard reinforcement learning implicitly assumes feasibility. This study introduces a feasibility-aware agentic reinforcement learning framestudy that explicitly reasons about whether a task can be completed within given cost budgets and failure risk limits. A feasibility estimator is trained to predict the probability that any valid action sequence exists under current constraints. The agent uses this signal to adapt its strategy, prioritize feasible subtasks, or terminate early when feasibility is low. Evaluation on 800–1,400 constrained web tasks demonstrates that feasibility-aware decision-making reduces wasted interactions, prevents high-risk attempts, and improves overall system reliability. This study reframes web automation as a constrained decision problem where recognizing infeasibility is as important as optimizing success.

Abstract: Background/Objectives: Critical limits represent quantitative thresholds of life-threatening diagnostic test results that require immediate clinician notification and may necessitate life-saving intervention to prevent adverse outcomes. Our goals are to report point-of-care critical limits for adults and newborns from a comprehensive U.S. national database, to identify POC instruments associated with the critical limits, and to support the harmonization of POCT practice. Methods: We gathered critical limit notification lists from 417 hospitals across all 50 states and Washington D.C., comprising university hospitals, trauma and heart centers, centers of excellence, community hospitals, and network hospitals. We extracted point-of-care critical limits, central laboratory critical limits (at hospitals with POC), adult international normalized ratio (INR) data, and instrument usage. Results: Low and high glucose critical limits (median values of 50 and 450 mg/dL, respectively) were the most frequently listed, reported by 73 hospitals (17.5%). Troponin was listed by ten hospitals, specified as troponin (n = 4), troponin I (n = 5), or “troponin TnI” (n = 1). Rarely, we encountered notification lists that assigned different critical limits to different instruments measuring the same analyte. Fifty-five hospitals did not specify instrument usage for any measurand on their notification list. The median differences in matched pairs of laboratory versus POC critical limits differed significantly (Wilcoxon signed-rank, P< 0.05) for low and high ionized calcium (N=21), low hemoglobin (N=23) and high INR critical limits for adults (N=27) and newborns (N=10). In some cases, matched pair analytes demonstrated identical critical limits. Conclusions: Harmonizing critical limit notification thresholds across point-of-care testing and different devices may improve consistency in clinical decision-making and patient outcomes. Despite the potential of POCT to shorten time to urgent intervention, relatively few hospitals currently include POCT critical limits on notification lists. Broader inclusion and transparent sharing of POCT critical values could harmonize practices across institutions, facilitate inter-institutional collaboration, and promote more timely and reliable responses to life-threatening diagnostic results.

Abstract: In horses, the risk of death is greatest during the first days of life. The objectives of this study were: 1) To describe the distribution of diagnoses and the case fatality rates among neonatal foals. 2) Within the septic foal population, to identify potential risk factors for mortality. Two hundred cases were included in the study. The median age on admission of the foals in the general population was 72 hours. The Arabian horse was the most common breed (82.4%). The general mortality rate was 24.5%, (95% Confidence lnterval (CI): 18.7-31.1). The top three diagnoses on admission were sepsis (22.5%), diarrhea (20.5%) and omphalitis (17%). Of the entire population of foals, 43% (95% CI: 36-50.2) were classified as septic by use of the definition in this study. The median age of septic foals on admission was 96 hours. The most common breed was Arabian (82.7%). The mortality rate among the septic foals was 30.2% (95% CI: 20.8-41.1). A foal that was diagnosed with sepsis on admission, was 4.3 times more likely to die or be euthanized compared to a foal that was not diagnosed with sepsis on admission.

Abstract: Purpose: To quantify second-law performance in a vertically oriented helically coiled tube heat exchanger (HCTHEX) and to develop predictive correlations for the dimensionless exergy-destruction fraction ϕ_D=E ̇_D/E ̇_(in,total)across a matrix of operating conditions and coil pitches. Methodology: A steady, real fluid-to-fluid CFD model was used with water on both shell and coil sides and laminar (or weakly transitional) treatment over the stated Reynolds-number range. Exergy rates at shell/coil inlets and outlets were computed for a heat-exchanger control volume and used to evaluate E ̇_Dand ϕ_D. Predictability was assessed via (i) global (non-pitch-specific) regressions including pitch as an explicit predictor, and (ii) pitch-specific regressions trained separately at each pitch; all models were trained in log space and evaluated using five-fold cross-validation. Findings: The global baseline power-law regression ϕ_D=A" " Re_shell^a Re_coil^b p^cyields statistically significant dependence on pitch and Reynolds numbers (e.g., for the D_h-based case: A=0.2238, a=0.04885, b=0.04982, c=0.7507). However, cross-validation shows limited predictive fidelity for the baseline (for D_h: R_(CV,log⁡)^2=0.1687, RMSE_(CV,log⁡)=0.1402). Among advanced surrogates, LogLog–GPR–ARDSE provides the best global performance for both characteristic-length definitions (for D_h: R_(CV,log⁡)^2=0.7171, RMSE_(CV,log⁡)=0.08181), representing a substantial reduction in prediction error relative to the baseline. Pitch-specific analysis demonstrates that the best advanced model depends on pitch: GPR–ARDSE is selected at p=1.80, 1.85, and 2.00, while bagged trees slightly outperform GPR at p=1.90and 1.95 under the minimum RMSE_(CV,log⁡)criterion. limitations: The reported correlations are calibrated to the simulated geometry family and operating ranges examined (including the pitch range studied) and should not be extrapolated beyond these conditions without additional verification. Practical implications: The resulting correlations enable rapid estimation of ϕ_Dfrom readily available nondimensional inputs (Re_shellⓜ,Re_coilⓜ,p), reducing the need for repeated full exergy accounting during design screening and operating-map exploration. Originality: This work couples a full fluid-to-fluid CFD exergy framework with systematic, cross-validated benchmarking of baseline power-law, advanced surrogate, and pitch-conditioned predictive models for ϕ_Din HCTHEX geometries, explicitly quantifying how model form and pitch conditioning affect predictive accuracy.

Abstract: As a motivation, a scenario is considered in which a Weinberg-type nonlinear extension may allow violations of the no-signaling constraint, making entanglement a potential resource for operational signaling. A minimal binary model is introduced: in each run, the sender selects a binary input bit and the receiver locally records a binary output bit. Signaling is defined operationally as a dependence of the local output statistics on the remote input and is summarized by a single channel parameter estimable from data. An estimator and a robust confidence interval are constructed to test the absence of signaling, and transmission reliability is quantified via the minimum decision error. Finally, a conservative criterion is proposed, based on an upper bound on the error and a threshold fixed a priori, to quantify a near-identity channel regime, together with minimal reporting requirements to rule out artifacts and classical leakage.

Abstract: Acute appendicitis is the most common surgical emergency. Due to its variable clinical presentation, diagnosis can be challenging, often leading to unnecessary imaging or surgery. The François score, a simple clinico-biological tool, aims to stratify patients by diagnostic probability. Objective: To evaluate the diagnostic accuracy of the François score in patients presenting with suspected acute appendicitis at the General Surgery Department of Habib Bougatfa Hospital, Bizerte, Tunisia. Methods: A prospective co-hort study evaluating diagnostic performance was conducted from October 2021 to April 2022. Patients aged over 15 years admitted for suspected acute appendicitis were included. The François score was calculated using predefined clinical and laboratory variables. Final diagnoses were based on histopathology or imaging. Diagnostic per-formance (sensitivity, specificity, predictive values, ROC curves) was analyzed. Re-sults: A total of 139 patients were included. The mean François score was 0.58±4.31. Appendicitis was confirmed in 128 patients (92.1%). A score ≥2 was significantly asso-ciated with acute appendicitis (OR = 8.29; p = 0.024). A score ≤ -6 was inversely corre-lated with appendicitis (OR = 0.004; p < 0.001). The score demonstrated an Area Under the Curve (AUC) of 0.90. At a cut-off of -6, the score yielded a sensitivity of 99.2% and a negative predictive value of 87.5%. The score stratified patients effectively, reducing unnecessary imaging and surgery. Conclusion: The François score is a reliable, low-cost diagnostic tool for evaluating suspected acute appendicitis. It can aid clini-cians in triaging patients and optimizing the use of imaging or surgical exploration.

Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder with limited disease-modifying therapies. Computational models can provide predictive insights into drug properties, although critically limited datasets pose challenges. Fifteen FDA-approved Parkinson’s disease drugs were represented as hydrogen-suppressed molecular graphs. Twelve degree-based topological indices were computed and used as descriptors for predicting seven physicochemical properties (MR, P, MV, MW, nHA, nRotB, Complexity). Multi-layer perceptron artificial neural network (ANN) and Random Forest (RF) models were trained. Model performance was evaluated using Leave-One-Out Cross-Validation (LOOCV). The statistical robustness of the models was verified using a Y-randomization test. Shapley Additive Explanations (SHAP) were applied for interpretability. The ANN demonstrated high predictive correlation on the small dataset for MR (R² = 0.876), P (R² = 0.875), MW (R² = 0.837), and nHA (R² = 0.901). Lower predictive performance was observed for MV (R² = 0.729), molecular Complexity (R² = 0.706), and nRotB (R² = 0.308). RF provided comparable results but was generally outperformed by ANN. The Y-randomization test yielded consistently negative average R²rand values (lowest R²rand = -1.708), confirming the absence of chance correlation. SHAP analysis identified the most influential topological indices for each property in ANN. ANN-based QSPR modeling with degree-based descriptors can accurately predict physicochemical properties of PD drugs for certain endpoints. These models were proven statistically robust through Y-randomization validation. Limitations include the small dataset size and high-dimensional descriptor space, highlighting the need for external validation, larger datasets, and inclusion of additional 3D/quantum descriptors for more complex pharmacokinetic endpoints.

Abstract: The rapid emergence of artificial intelligence (AI) language models has generated intense debate regarding their appropriate role in scholarly communication. Critics frequently argue that AI-assisted writing undermines intellectual authenticity by bypassing the traditional labor associated with authorship. This commentary proposes an analogy between AI-assisted writing and laboratory-grown diamonds. Both produce artifacts that are materially indistinguishable from their traditional counterparts—classically written prose and mined diamonds—yet provoke cultural discomfort because their provenance differs. By examining this analogy through the lenses of technological history, epistemic responsibility, and evolving definitions of craftsmanship, this paper argues that resistance to AI-assisted writing largely reflects cultural attachment to narratives of effort rather than objective differences in intellectual value. Historical parallels—including the adoption of statistical software, word processors, and digital literature databases—demonstrate that scholarly practices often undergo initial moral panic followed by normalization. AI does not eliminate authorship but relocates the locus of scholarly mastery from mechanical production toward conceptual clarity, judgment, and interpretive accountability. The critical ethical question is therefore not whether AI tools participate in writing, but whether authors retain responsibility for accuracy, reasoning, and intellectual integrity. Understanding this shift may help academic institutions develop policies that promote transparency without conflating technological assistance with intellectual fraud.

Abstract: Tooth development or odontogenesis is a complex morphogenetic process that requires tightly regulated interactions between the oral epithelium and mesenchyme of neural crest origin. In this narrative review we compile existing knowledge regarding gene regulatory networks and epigenetic factors throughout tooth development from initiation to eruption. Signaling between epithelium and mesenchyme is mediated by four conserved pathways—Wnt/β-catenin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Sonic hedgehog (Shh)—which operate iteratively and interact through extensive crosstalk at each developmental stage. Transcription factors such as PAX9, MSX1, PITX2 and LEF1 interpret these signals to control cell fate decisions and differentiation. Epigenetic modifications, including DNA methylation, histone modifications, and microRNA-mediated regulation, provide additional layers of control that fine-tune gene expression programs. Unlike existing reviews that address these regulatory mechanisms separately, here we integrate signaling pathways, transcription factor networks, epigenetic regulation, human genetic disorders, dental stem cell biology, and recent single-cell transcriptomic insights into a unified framework. We discuss opportunities to apply developmental biology knowledge towards regenerative dentistry goals, including iPSC-derived dental models and spatially resolved multi-omics approaches, while acknowledging the considerable gap between preclinical findings and clinical application.

Abstract: Background: Tumor microenvironments (TMEs) frequently exhibit extracellular acidity (pH ~6.5), a biophysical feature known to play a critical role in cellular behavior, tumor progression, immune suppression, and altered therapeutic response. While synthetic regulatory circuits capable of sensing acidity have been proposed, quantitative frameworks describing how microenvironmental pH dynamics interact with tumor–immune systems remain limited. Methods: We developed a computational modeling framework describing acidity-mediated regulatory dynamics in coupled tumor–immune systems. The model integrates interacting processes including tumor population dynamics, effector T-cell activity under acid-dependent suppression, regulatory vector dynamics, pH-responsive promoter activation, buffering or alkalinization mechanisms, cytokine-mediated feedback, and proton concentration kinetics calibrated to physiological pH ranges (6.0–7.4). Alternative acidity-modulating strategies, including substrate-dependent and substrate-independent buffering mechanisms, were examined through parameter sweeps, sensitivity analysis, and spatial reaction–diffusion extensions. System behavior was analyzed using stability and regime characterization methods. Results: The model exhibits distinct dynamical regimes in which acidity modulation reshapes tumor–immune interactions. Simulation of the acidity-responsive regulatory module demonstrated that promoter-driven therapeutic activation reduces tumor burden through two mechanistically distinct pathways. The alkalinization strategy elevated steady-state pH (ΔpH ≈ 0.2–0.6), partially restoring immune activity and reducing tumor persistence via microenvironmental feedback. In contrast, immune reactivation enhanced cytotoxic pressure directly, producing more rapid tumor suppression without substantially normalizing extracellular pH. In both architectures, therapeutic output increased under acidic conditions and diminished as pH approached physiologic levels, demonstrating dynamically coupled and self-limiting behavior. Sensitivity and scaling analyses further revealed hierarchical parameter control and architectural differences between substrate-dependent and substrate-independent buffering mechanisms. Conclusions: This study provides a quantitative theoretical framework for understanding how microenvironmental acidity functions as a regulatory variable in tumor–immune dynamics. The results highlight generalizable principles governing acidity-mediated feedback, system stability, and scaling behavior, offering mechanistic insights relevant to microenvironment-responsive regulatory systems. These findings emphasize the importance of biophysical microenvironmental factors in shaping cellular system dynamics and provide a basis for future experimental investigation of acidity-responsive biological regulation.

Abstract: Understanding how gradient descent shapes neural network representations remains a fun-damental challenge in deep learning theory. Recent work has revealed that neural networks behave as “racing” systems: neurons compete to align with task-relevant directions, and those that succeed experience exponential norm growth. However, the geometric principles govern-ing this race—particularly when data lies on low-dimensional manifolds and networks employ adaptive normalization—remain poorly understood. This paper establishes a mathematical framework that unifies and extends these insights. We prove three fundamental theorems: (1) neuron weight vectors converge exponentially to the tangent space of the data manifold, with a rate determined by local curvature and gating dynamics; (2) for rotation-equivariant tasks, an angular momentum tensor is conserved under gradient flow, imposing topological constraints on neuronal rearrangements; (3) the distribution of high-norm “winning” neurons follows a von Mises-Fisher concentration on the manifold, with concentration parameter linked to initial angular variance. As a case study, we integrate Bayesian R-LayerNorm—a provably stable nor- malization method—into our framework, deriving a modified norm growth law that explains its empirical robustness on corrupted datasets. Together, these results provide a geometric foun-dation for understanding capacity adaptation, lottery tickets, and uncertainty-aware learning in neural networks.

Abstract: (1) Background: Access to reliable medicines information is essential to support safe medi-cine use during pregnancy and breastfeeding, where concerns regarding fetal and neonatal safety complicate clinical decision-making. Analgesics are widely used during these peri-ods, yet uncertainty regarding safety persists due to evolving evidence, regulatory changes, and inconsistent information sources. Obstetric medicines information services play a critical role in addressing these information needs. This study aimed to evaluate patterns and characteristics of analgesic-related enquiries to a specialist obstetric medicines infor-mation service over a 20-year period. (2) Methods: A retrospective observational study was conducted using enquiry data from the King Ed-ward Memorial Hospital Obstetric Medicines Information Service (KEMH OMIS), Western Australia. All enquiries recorded between 1 January 2001 and 31 December 2020 were ex-tracted from the Microsoft Access® database. Records with incomplete data were excluded. Data were standardised, coded, and analysed using Microsoft Excel® and SPSS® Version 25. Descriptive statistics were used to summarise enquiry characteristics, caller type, tim-ing of exposure, and analgesic medicines involved. Trends over time were analysed. (3) Results: A total of 48,458 enquiries were analysed, of which 4,978 (10.3%) related to anal-gesics, making this the third most common medicine class. Most enquiries related to breastfeeding (62.1%), followed by pregnancy (32.7%). The public accounted for 60.9% of calls, while health professionals contributed 39.1%. The highest frequency of breastfeeding enquiries occurred within the first four weeks postpartum, and pregnancy enquiries were most common in the second trimester. Paracetamol was the most frequently enquired an-algesic (24.5%), followed by codeine (19.8%), ibuprofen (14.4%), diclofenac (7.2%), and tramadol (9.3%). Analgesic-related enquiries declined significantly over time (p< 0.001), particularly codeine-related enquiries following regulatory safety warnings. (4) Conclusion: Analgesics represent a substantial proportion of medicines information enquiries in preg-nancy and breastfeeding, reflecting widespread use and ongoing safety concerns. Pharma-cist-led medicines information services play a critical role in supporting safe analgesic use. Continued surveillance and targeted education are essential to optimise maternal and in-fant medication safety.

Abstract: Background: Depression is a major public health concern and remains a challenge despite traditional care approaches. This study aimed to describe perceptions of changes associated with depressive experience reported by participants in a program grounded in Permission Theory. Methods: This exploratory and descriptive study employed a quantitative and qualitative approach, grounded in Bardin’s Content Analysis to analyze 23 spontaneous accounts from participants who reported experiences related to depression. The participants evaluated their lives before and during the program. Results: The quantitative analysis showed an increase in self-reported scores of overall life evaluation during the program. Overall, the participants’ accounts indicated that they subjectively perceived changes in emotional, relational, and functional aspects of their everyday lives. Conclusions: These findings emphasize how participants interpret and describe changes in their emotional, relational, and functional lives, aspects that are often less visible in conventional mental health outcome research. These perceptions do not allow for inference of clinical effects or a causal relationship with program participation, reinforcing the need for controlled studies to investigate potential impacts on mental health outcomes.