Abstract:

Introduction: Recent guidelines have emphasized the importance of early mobilization and rehabilitation of patients following cardiac surgery. However, studies on the optimal targets and prescription methods for phase I cardiac rehabilitation (CR) are lacking.Purpose: This study aimed to evaluate the feasibility and utility of an early phase 1 submaximal cardiopulmonary exercise test (CPET) using a recumbent ergometer in patients who have undergone cardiac surgery. Methods: Twenty ambulatory patients who underwent cardiac surgery between December 2021 and February 2023 were referred to the CR department on the fifth postoperative day, and a CR program was initiated. The program was conducted five times a week, with hour-long sessions consisting of warm-up exercises, resistance training, aerobic exercises, and a cool-down period. A recumbent ergometer-based submaximal CPET was performed approximately nine days after the surgery, prior to discharge. Participants initiated the test at 0 W, and the workload was increased by 20 W after 2 minutes. During the test, researchers evaluated parameters including estimated peak values of oxygen consumption (VO2), metabolic equivalents of task, respiratory exchange ratio (RER), blood pressure, heart rate (HR), and rating of perceived exertion (RPE). The grip strength test, 6-minute walk test (6MWT), Korean Activity Scale/Index (KASI), EuroQol-5 dimension (EQ-5D), and short-form 36-item health survey (SF-36) values were also measured prior to discharge. Results: Twenty patients (75% male, average age 62.50 ± 1.99 years) underwent CPET at a median of 9.0 (8.0; 12.5) days postoperative. The average exercise duration of the CPET was 411.75 ± 168.25 seconds. During the test, their estimated peak VO2 was 12.32 ± 0.75 ml/kg/min (corresponding to 46.65 ± 2.08% of VO2 max). The estimated peak RER was 1.01 (0.98–1.12), and the estimated peak RPE was 15.00 ± 0.51. Furthermore, the estimated peak HR was 111.8 ± 3.76 beats/min (equivalent to 70.95 ± 2.09% of age-predicted maximal HR). After adjustment for age and sex, significant positive correlations were observed between the estimated peak VO2 and 6MWT, squat endurance test, KASI, EQ-5D, and the physical component summary (PCS) of the SF-36 questionnaire. The 6MWT, squat endurance test, KASI, and PCS of SF-36 showed a correlation coefficient (r) of 0.522 (p=0.026), 0.628 (p=0.005), 0.586 (p=0.011), and 0.546 (p=0.019), respectively. No significant cardiac events, such as ST elevation/depression or hemodynamic instability, were observed during the test.Conclusion: Our findings suggest that performing recumbent ergometer-based CPET during early phase 1 CR is safe and feasible. These results highlight the potential of recumbent ergometer-based CPET as a valuable tool for guiding the appropriate prescription of early CR programs following hospital discharge in patients undergoing cardiac surgery.

Abstract: The rising global prevalence of obesity and related disorders, including metabolic syn-drome (MetS) and type 2 diabetes (T2DM), highlights the need to better understand the mechanisms underlying these conditions, particularly host–microbiota interactions. While the gut microbiota has been extensively studied, the role of the oral microbiota and its interaction with human salivary proteins remains poorly explored. This study investigated the integrated human salivary proteome and bacterial metaproteome in Brazilian individuals spanning different metabolic states: normal weight, overweight, obesity, MetS, and T2DM. Saliva samples were analyzed using mass spectrome-try-based proteomics to identify differential protein profiles. The results revealed sig-nificant downregulation of the human proteins MYSM1 and GAD65 in obesity, MetS, and T2DM, with negative correlations to BMI, suggesting compromised an-ti-inflammatory functions. In contrast, carbonic anhydrase VI (CA6) was markedly upregulated and positively correlated with systolic blood pressure and glucose levels, indicating an acidic and inflammatory oral environment. In the bacterial metaprote-ome, TrxC-2, UMPK, and RsmH were significantly increased in metabolically com-promised groups and positively associated with anthropometric and insulin resistance markers, reflecting microbial adaptations to oxidative stress and enhanced virulence. Interactome analysis revealed negative correlations between bacterial proteins and MYSM1/GAD65, alongside positive associations with CA6, suggesting a feedback loop between oral dysbiosis and host metabolic dysfunction. These findings highlight the oral cavity as a key site of host–microbiota interaction in metabolic diseases and iden-tify potential biomarkers and therapeutic targets.

Abstract: Epiderm is composed by specific layers, functions and implications in the life The purpose of the study is to analyse and to identify structural characteristics reffering to melanocytic nevi, in youth patients. Using both optical and electronic microscope, could be possible a better describtion related specificity in melanocytic nevi characteristics. Future trends, are important key points in management, including preventive and prophylactic methods.

Abstract: Reproductive dysfunction in captive‐bred males of the flatfish Solea senegalensis re-mains a major bottleneck for its aquaculture. To clarify the molecular basis underlying these impairments, we performed an integrated analysis of transcriptomes, proteomes and methylomes from gonads of wild-type individuals and first-generation (F1) cap-tive fish of both sexes. Nineteen RNA-seq libraries and eighteen LC–MS/MS proteomes were generated, allowing the quantification of more than 32,000 genes and 2,221 pro-teins. Differential expression and principal component analyses revealed that sex was the primary driver of molecular variation, whereas origin (F1 vs. wild-type) had a more moderate effect. Multi-omics integration showed a partial and compari-son-dependent correspondence between RNA and protein levels, with a marked RNA–protein decoupling in F1 males. Despite this limited concordance, functional enrich-ment analyses identified consistent regulation of key biological processes, including translation, energy metabolism, and reproductive pathways such as gametogenesis, fertilization, and early embryonic development. Within this regulatory framework, previously characterized DNA methylation landscapes in gonadal tissue suggest an additional epigenetic layer modulating the transcriptional potential of reproductive genes, particularly in captive-bred males. F1 males exhibited coordinated down-regulation of reproductive functions across omic layers, consistent with altered post-transcriptional and post-translational regulation. Overall, this study provides the first comprehensive multi-omics framework integrating transcriptomic, proteomic, and epigenetic information in S. senegalensis gonads, offering mechanistic insights in-to the molecular basis of reproductive dysfunction in F1 broodstock and supporting future strategies to improve reproductive performance in aquaculture.

Abstract: Early childhood caries (ECC) is routinely described as a complex, multifactorial disease shaped by biofilm ecology, host susceptibility, diet, behavior, and social context. Yet, a growing strand of public-health messaging and implementation practice increasingly treats ECC as a one-step problem solvable by a topical “magic paint” (most prominently silver diamine fluoride, SDF) and deliverable by non-dental or minimally trained providers. This commentary argues that the core contradiction—declaring ECC polycausal while operationalizing it as monocausal—drives a harmful evidence-to-policy drift: research designs favor short-term, easily marketable surrogate endpoints (e.g., “arrest” defined partly by SDF-induced black staining) and implementation strategies shift diagnosis and management to underprepared personnel without robust guardrails.Using a journal-style critical lens anchored in ROB-2, CONSORT, and STROBE principles, I examine recent Canadian work frequently cited to justify “paint-and-go” approaches, including open-label randomized trials of SDF application intervals and microbiome-focused substudies, and I integrate the delegation axis through the Canadian Caries Risk Assessment Tool (CCRAT) and its embedding into primary care workflows. While SDF and non-dental screening can be valuable adjuncts in a continuum of care, overselling them as substitutes for dentist-led diagnosis, pulpal assessment, and definitive rehabilitation risks institutionalizing a two-tier standard for children—especially for Indigenous and remote communities. I conclude with concrete research and policy guardrails: comparator-driven trials, multilevel modeling, lesion-specific sampling where mechanistic claims are made, patient-centered outcomes, defined referral timelines, and a dental-home–anchored pathway that treats SDF as a bridge—not a destination.

Abstract: This research presents a comprehensive framework for optimizing Electric Vehicle (EV) charging infrastructure along the Lake Michigan circuit (LMC) in Michigan to support ecotourism, considering both slow charging at destinations and fast charging along the corridor. The framework identifies the optimum location and number of Level 2 chargers and Direct Current Fast Chargers (DCFC), using heuristic algorithms. The study evaluates infrastructure planning based on four key objectives: (1) minimizing overall charging infrastructure costs, (2) reducing grid network upgrade costs, (3) providing an acceptable level of service to long-distance travelers using DCFCs by minimizing queuing delays and deviations from their intended routes, and (4) improving destination charging to mitigate battery degradation by minimizing unserved charging demand from Level 2 chargers redirected to DCFCs. The integration of Level 2 and DCFC networks facilitates strategic investment by effectively managing charging demand, allowing unserved Level 2 demand to be accommodated at DCFC stations while adhering to budgetary constraints. The results show that increasing the budget from $15 to $20 million reduces user inconvenience by 47%, while a further increase to $25 million yields an additional 18% reduction. Additionally, increasing users’ value of time from $13 to $36 per hour results in a 50% reduction in average queuing time.

Abstract: The deployment of agentic AI systems—multi-agent orchestrations, tool-calling pipelines, and autonomous planning architectures—introduces operational instabilities that cannot be attributed to interconnect limitations or runtime control conflicts alone. Even in systems with adequate infrastructure and coherent control planes, cost escalation, non-deterministic behavior, and soft degradation persist, pointing toward semantic coupling as a distinct failure domain. This article argues that agentic system stability—the degree to which autonomous agent decisions across planning, tool selection, execution, and verification layers remain mutually consistent with respect to shared intent—constitutes a structural property whose loss gives rise to economically significant inefficiencies. Classical metrics fail to capture stability loss because conflicts between agentic layers are semantically distributed, emergent, and do not manifest as discrete faults. The contribution of this work is a structural problem analysis that positions agentic incoherence as a first-order economic and operational variable, complementing prior analyses of interconnect-induced instability and control plane incoherence. The methodology is deliberately conceptual, avoiding implementation details, framework evaluations, or prescriptive solutions.

Abstract: Cancer represents a dynamic and evolving ecosystem driven by complex interactions among genetically and phenotypically diverse cell populations. Within the tumor microenvironment, cells engage in both competitive and cooperative behaviors that determine their collective evolutionary fate. To capture these dynamics, we employ evolutionary game theory to investigate the coexistence and adaptation of four representative tumor phenotypes: proliferative (P), invasive (I), resistant (R), and cooperative (C). Using a four-strategy evolutionary game-theoretic framework, we show that explicitly including a cooperative phenotype qualitatively expands the range of polymorphic and noise-sustained coexistence regimes observed in the model, enabling coexistence regimes that cannot arise in reduced three-strategy models. Numerical simulations reveal that frequency-dependent selection promotes stable polymorphisms or oscillatory coexistence among phenotypes, explaining persistent intratumoral heterogeneity. Incorporating stochastic replicator equations further demonstrates that random fluctuations can sustain rare phenotypes, induce transient dominance shifts, and generate noise-driven evolutionary transitions. To explore environmental modulation, we extend the model to analyze tumor evolution under acidic microenvironmental conditions and under pH-buffered therapeutic interventions. Acidity enhances the fitness of invasive and resistant cells, driving the system toward aggressive, therapy-tolerant equilibria. In contrast, buffering restores cooperative and proliferative dominance, illustrating that ecological control of the tumor microenvironment can redirect evolutionary trajectories. Collectively, this work unifies deterministic and stochastic evolutionary game theory approaches to show how tumor heterogeneity arises from eco-evolutionary feedbacks, stochastic fluctuations, and environmental pressures. The results suggest that evolution-informed, microenvironment-modulating interventions may influence selective pressures in ways that favor less aggressive evolutionary outcomes, providing a conceptual basis for adaptive therapeutic strategies.

Abstract: Cities play a central role in shaping societal responses to the climate crisis, concen-trating both climate risks and institutional capacity to address them. While climate impacts are widely distributed, they are experienced unevenly, with marginalized populations facing disproportionate exposure to economic disruption and environ-mental stress, particularly in urban environments. This review article examines how cities can enhance climate resilience while supporting a just transition to a post-carbon economy. It addresses three interrelated questions: how vulnerable urban populations can be better prepared for green employment; how transformations in work and commuting can promote compact, mixed-use, and transit-friendly urban districts; and how such districts can be designed to protect residents from urban heat and improve walkability through shade and nature-based solutions. The analysis synthesizes find-ings from recent empirical studies and applied policy initiatives, including a municipal green-employment pilot in Tel Aviv-Yafo, the “Reinventing Paris” office-to-housing program, and urban heat and pedestrian-behavior research. Together, these cases il-lustrate how physical adaptation strategies interact with labour-market dynamics and social policy. The review concludes that effective urban climate resilience requires in-tegrating infrastructural and spatial interventions with labour-market transformation, social protection, and inclusive governance, positioning cities as key operational units for advancing equitable climate action.

Abstract: This paper develops a structural causal model to quantify how anti-money-laundering (AML) policy adjustments influence both detection performance and financial-system stability. The model integrates regulatory thresholds, monitoring rules, bank-level reporting behaviors, and macro-prudential indicators. Panel data from 23 banks over 10 years, comprising 1.2 billion transactions, were used for parameter estimation and scenario simulation. Tightening suspicious-activity thresholds increased estimated detection rates by 18–24% but reduced liquidity coverage ratios by up to 3.6% for smaller institutions. A balanced scenario combining moderate threshold adjustments with targeted monitoring improved detection by 15.0% while limiting liquidity impact to 1.1%. The framework quantitatively illustrates the trade-off between surveillance strength and system-wide stability.

Abstract: Financial crimes, including money laundering, fraud, and terrorism financing, remain persistent threats to financial systems due to the increasing sophistication of perpetrators and their extensive use of layering (tumbling) techniques to obscure transaction trails. Conventional machine learning–based anomaly detection systems often exhibit high false negative rates, particularly in streaming financial environments where transaction behaviors evolve dynamically. This study proposes a Systematic Detection Learning framework for real-time identification of layering activities in financial transaction data. The framework employs a user-centric, step-wise analytical process that systematically structures transaction attributes to extract recurring behavioral patterns associated with layering. Using SFinDSet for Systematic Detection of Financial Crimes, a publicly available financial crime dataset hosted on Kaggle, the proposed model is evaluated against established anomaly detection, classification and clustering techniques, including Isolation Forest, One-Class Support Vector Machine (O-C SVM), and Online k-Means. Performance evaluation focuses on the detection of layering instances, identification of unique layerers, and consistency across models. Experimental results show that the Systematic Detection approach identifies 7,694 confirmed layering instances and 441 unique layerers, thus outperforming Isolation Forest (with 99.54% consistency), Online k-Means (with 78.91%), and O-C SVM (27.43%). The results demonstrate that the proposed framework significantly reduces false negatives while maintaining high detection accuracy. By leveraging structured domain knowledge alongside adaptive learning, the Systematic Detection model provides a robust and interpretable benchmark for layering detection in streaming financial data. This research contributes an effective and scalable framework that can be integrated with machine learning techniques to enhance real-time financial crime detection and mitigation.

Abstract: Retrieval-Augmented Generation (RAG) systems face substantial challenges when navigating large volumes of complex scientific literature while maintaining reliable semantic retrieval, a critical limitation for automated scientific discovery where models must connect multiple research findings and identify genuine knowledge gaps. This study introduces a question-based knowledge encoding method that enhances RAG without fine-tuning to address these challenges. Recognizing the lack of syntactic understanding in major Large Language Models, we generate syntactic and semantic-aligned questions and apply a syntactic reranker without training. Our method improves both single-hop and multi-hop retrieval with Recall@3 to 0.84, representing a 60% gain over standard chunking techniques on scientific papers. On LongBenchQA v1 and 2WikiMultihopQA, which contain 2000 documents each averaging 2k-10k words, the syntactic reranker with LLaMA2-Chat-7B achieves F1 = 0.52, surpassing chunking (0.328) and fine-tuned baselines (0.412). The approach additionally reduces vector storage by 80%, lowers retrieval latency, and enables scalable, question-driven knowledge access for efficient RAG pipelines. To our knowledge, this is the first work to combine question-based knowledge compression with explicit syntactic reranking for RAG systems without requiring fine-tuning, offering a promising path toward reducing hallucinations and improving retrieval reliability across scientific domains.

Abstract: Justicia spicigera is a central medicinal plant in Mexican ethnomedicine, yet its therapeutic potential against prostate cancer remains largely unexplored. This study investigated the antiproliferative and pro-apoptotic effects of a 50% hydroalcoholic extract from the leaves and stems of J. spicigera on androgen-sensitive LNCaP prostate cancer cells. Phytochemical profiling via TLC and LC-MS putatively identified the bioactive flavonoid kaempferitrin within the complex extract. Biological assays, including MTT, trypan blue exclusion, and flow cytometry, revealed that the extract inhibits LNCaP proliferation in a distinct, dose-dependent manner. At a lower concentration (250 µg/mL), the extract exerted a primarily cytostatic effect by inducing significant G₀/G₁ cell cycle arrest without triggering immediate cell death. Conversely, higher concentrations (≥500 µg/mL) were potently cytotoxic, reducing cell viability to below 20% and inducing late apoptosis in approximately 58% of the population within 24 hours. These results validate the biological activity of J. spicigera in a prostate cancer model and suggest that the extract, or its constituent flavonoids could serve as a template for developing treatments that target both cell cycle checkpoints and programmed cell death.

Abstract: Background: Pediatric myopia is highly prevalent in Asian populations and has been associated with scleral extracellular matrix remodeling. Doxycycline hyclate, a tetracy-cline-class compound with matrix metalloproteinase–inhibitory activity at sub-antimicrobial doses, is under investigation for long-term ophthalmic use. Methods: A GLP-compliant 9-month repeat-dose ocular toxicity study (Study No. 2098-005) was conducted in juvenile Dutch Belted rabbits. Animals received topical bilateral admin-istration of doxycycline hyclate ophthalmic solution (0.01%, 0.1%, or 0.5%) twice daily, followed by a recovery period. Evaluations included clinical and ophthalmic observa-tions, intraocular pressure, clinical pathology, toxicokinetics, and gross and microscopic pathology. Results: No treatment-related mortality or adverse clinical findings were observed. Body weight gain, ophthalmic examinations, intraocular pressure, and clinical pathology parameters showed no test article–related effects. Systemic exposure in-creased with increasing dose, with low accumulation following repeated administration and no apparent sex-related differences. No test article–related macroscopic or micro-scopic findings were identified. Conclusion: Long-term topical administration of doxycycline hyclate ophthalmic solution was well tolerated in juvenile rabbits, sup-porting its further clinical development for chronic ophthalmic indications.

Abstract: A radical epistemological reinterpretation of classical mechanics through the formal apparatus of dynamic system identification theory is proposed. Using rigorous definitions from Ljung (1999) --- data informativeness, persistent excitation, Fisher information matrix, and Hankel rank --- it is demonstrated that Newton's laws represent boundaries of information extraction from observations, not ontological statements about reality. The first law is reformulated as data uninformativeness under zero excitation ($\operatorname{rank}(\bar{F}) = 0$). The second law emerges from asymptotic variance of estimates: mass as the conditioning parameter ($\operatorname{Var}(\hat{m}) \propto m^4$). The third law is interpreted as self-consistency for closed systems with finite Hankel rank. It is shown that momentum is the conserved coefficient at $1/s$ in spectral decomposition, energy is the invariant quadratic norm preserved by norm-preserving evolution operators, and coordinates are indices of spectral modes, with center of mass as the unique minimal-rank parameterization. For rotational dynamics, it is demonstrated that phase loss under rotation transforms Fourier modes into Bessel functions, with Bessel zeros marking fundamental identifiability boundaries ($\mathcal{I} = 0$, Cram'er-Rao bound $= \infty$). The Dzhanibekov effect is reinterpreted as an informational event: temporary loss and stochastic restoration of orientation identifiability, yielding testable predictions about observer-dependence. A detailed case study of the lighthouse problem illustrates how identifiability boundaries emerge in practice: spatial observations alone yield a $b \cdot \omega$ degeneracy, resolvable only through extended sensor arrays providing three independent information channels (spectral frequencies, spatio-temporal delays, spatial distribution). It is proved that discrete source configurations are fundamentally limited to $K_{\max} \sim \log(\omega_{\max}/\omega_{\min})/\log M_{\max}$ distinguishable sources due to spectral crowding, while continuous configurations achieve infinite Hankel rank. The variational optimization problem of maximizing Fisher information under geometric constraints yields differential rotation on logarithmic spirals as the unique optimal solution, explaining the ubiquity of spiral structures in nature. The James--Stein phenomenon at $d=2$ is reinterpreted as a physical channel constraint: the electromagnetic observation pathway fundamentally limits identifiability to two dimensions, finding rigorous algebraic foundation in Drozd's trichotomy theorem which classifies finite-dimensional algebras as finite, tame, or wild, with the latter --- characterized by two or more independent parameters --- rendering identification fundamentally impossible. Pulsars serve as natural laboratories for testing these predictions, where quasi-periodic timing structures provide empirical arbitrators of the theory. A deep mathematical correspondence is established between the lighthouse problem and optical diffraction: rotational averaging in both cases produces Bessel functions, with Airy disks and identifiability boundaries arising from the same spectral topology defined by Bessel zeros. A parable illustrates how all mechanical concepts emerge from minimal observational capabilities: a physicist in total darkness with seeds, two ears, and a rotating chair reconstructs "space", "mass", and "time" purely from identification constraints. Duality as a boundary and D4 according to Dynkin.

Abstract: Light exposure is a primary zeitgeber for the human circadian system and plays a key role in shaping sleep–wake patterns during adolescence, a period marked by biological sensitivity and social constraints. How the temporal organization and spectral composition of daily light exposure differ between weekdays and weekends remains poorly un-derstood. Eighteen adolescents (15–17 years) were monitored for seven days using wrist actigraphy with integrated light sensors. Sleep parameters, nonparametric circadian rhythm indices, and time-resolved profiles of ambient and spectral (blue, green, and red) light exposure were analyzed. Repeated-measures ANOVA tested the effects of time of day and day type. Total sleep time and time in bed were longer on weekdays than on weekends (p < 0.05), while sleep latency and WASO did not differ. Circadian indices indicated preserved rhythmic organization. Light exposure showed a robust diurnal profile, with higher spectral irradiance on weekends (p < 0.001), especially in the morning and early afternoon. Significant time × day-type interactions were observed across all spectral bands (p < 0.001), indicating systematic reshaping of daily light profiles. Ado-lescents exhibit weekday–weekend differences in the temporal and spectral organization of light exposure, affecting the amplitude and shape of overall daily profiles.

Abstract: Human-elephant conflict (HEC) has become a major conservation and socio-economic challenge across Asia, particularly in elephant range countries, due to rising human encroachment and habitat loss. In India, HEC escalation is linked to habitat degradation, agricultural expansion, and linear infrastructure development. In the West Singhbhum district of Jharkhand state, changes in LULC from deforestation, mining, and agricultural encroachment have severely altered elephant habitats. The loss of migratory routes has forced elephants to remain in disturbed areas, intensifying conflict. A three-year field study (2018–2020) across Porahat, Chaibasa, Kolhan, and Saranda Forest Divisions, combined with two decades (2000–2020) of LULC analysis, recorded 157 human deaths, 2837.90 acres of crop damage, 1925 house destructions, 3146 quintals of grain loss, and 35 elephant deaths, including nine poaching cases. Dense vegetation declined from 49.14% (2000) to 28.68% (2020), while sparse vegetation and agricultural land increased by 15.35% and 3.68%, respectively. Water bodies decreased by 0.33%, and barren land increased by 3.70%. Core forests (>500 acres) reduced by 16.86%, with forest perforation increasing by 15.69%. Only 6.7% of the district remains suitable for elephants, mostly in Saranda (44.2%), while NDVI shows 83.54% non-favourable change. Ensuring coexistence demands improved landscape connectivity and targeted conservation strategies, while exclusion zones need site-specific mitigation measures.

Abstract: Background: Population aging poses a growing public health challenge in low- and middle-income countries, including Indonesia. Functional independence is a key determinant of older adults’ quality of life, yet integrated community-based health promotion models addressing this issue remain limited. Objective: This study developed and empirically validated an Integrated 5I Health Promotion Model (Identify, Inspire, Initiate, Integrate, and Impact) to enhance independence and quality of life among pre-older adults and older adults in an urban Indonesian setting. Methods: A community-based cross-sectional survey was conducted among 240 pre-older adults and older adults in Surakarta, Indonesia, using proportional cluster sampling from community activity groups. The Integrated 5I Model was constructed based on the Health Belief Model, the Logic Model, and a pentahelix approach. Data were collected using a structured questionnaire and analyzed using path analysis to examine direct and indirect relationships among internal and external factors, perceptions, participation, independence, and quality of life. Results: The model demonstrated good structural fit and explained a substantial proportion of variance in independence and quality of life. Perception and participation played significant mediating roles between internal and external factors and independence. Increased independence was significantly associated with improved quality of life among older adults. Participation showed the strongest direct effect on physical independence (β = 3.018, p < 0.001), while independence significantly predicted quality of life (β = 0.599, p < 0.001). The model demonstrated excellent fit (CFI = 1.000; RMSEA = 0.000; SRMR = 0.012). Conclusions: The Integrated 5I Health Promotion Model provides a pragmatic and scalable framework for community-based interventions designed to promote independence and quality of life among aging populations in urban low- and middle-income settings. This model has important implications for public health programs and policies targeting healthy and active aging.

Abstract: Aiming to better understand how botanical products affect non-target organisms, the pre-sent work reviews current literature focusing on the toxicity of botanical pesticides to or-ganisms other than targeted pests, in order to trace a panorama on the future of sustaina-ble agricultural models worldwide, considering the importance of ecotoxicological studies in the development of new pesticides, including the botanical kinds, which are commonly recognized as essentially harmless. The article reviews published works gathered from digital databases and highlights modern tendencies in pest management research and the development of novel bioinputs, while discussing the Brazilian current legislature re-garding agricultural innovations and field obstacles. Nanotechnology techniques are dis-cussed as major innovations employed in the pest control field, and their employment in the improvement of botanical pesticides is addressed and explored. In this work we ana-lyze the factors involved in determining the success of botanical products and their im-portance to the implementation of a more sustainable way to manage crops. The results indicate a significant lack of studies focused on effects of botanical products on non-target organisms, and an increase in studies with nanoformulations.

Abstract: Background: Stroke remains a leading cause of mortality and long-term disability globally, necessitating effective primary prevention strategies. While machine learning (ML) models offer superior predictive capabilities compared to traditional linear risk scores, their application in clinical practice is often hindered by the "class imbalance" problem, where the rarity of stroke events leads to biased, low-sensitivity models. Furthermore, the literature currently lacks rigorous head-to-head benchmarking of modern boosting algorithms on moderate-sized clinical datasets. This study aimed to identify the optimal predictive model for stroke by systematically benchmarking seven ensemble algorithms and validating their clinical utility using Decision Curve Analysis (DCA).Methods: We analyzed a retrospective multi-center cohort of 5,110 patients, characterized by a severe class imbalance (4.9% stroke incidence). Feature engineering included the encoding of sociodemographic determinants and clinical biomarkers. We conducted a rigorous 10-fold stratified cross-validation tournament to compare seven classifiers: Linear Discriminant Analysis (LDA), Extra Trees, AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost. Performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC) and Brier Score for calibration. To address clinical safety, decision thresholds were optimized to maximize sensitivity. Clinical utility was assessed using Decision Curve Analysis to quantify net benefit across relevant risk thresholds.Results: The classical Gradient Boosting Classifier emerged as the top-performing model, achieving a mean AUC of 0.842 (95% CI: 0.82–0.86). It statistically outperformed both the linear baseline (LDA, AUC=0.833) and complex modern implementations such as XGBoost (AUC=0.787) and Extra Trees (AUC=0.748). By tuning the decision threshold to 0.01, the champion model achieved a screening Sensitivity of 86.0% and Specificity of 53.6%. SHAP (SHapley Additive exPlanations) analysis identified Age, Average Glucose Level, and BMI as the dominant non-linear predictors. Crucially, Decision Curve Analysis demonstrated that the Gradient Boosting model provided a higher net clinical benefit than "treat-all" or "treat-none" strategies across threshold probabilities of 1% to 40%.Conclusion: Contrary to current trends favoring deep learning or complex boosting implementations, classical Gradient Boosting architectures demonstrated superior generalization on imbalanced tabular clinical data. The developed model combines high discriminatory power with proven clinical utility, supporting its deployment as an automated, high-sensitivity screening tool in primary care settings.