Search Results (9,837)

Community-based digital transformation (DX) initiatives are increasingly used to promote healthy aging, although their effects are often evaluated using subjective self-reported measures. This study aimed to quantitatively evaluate a community DX health promotion initiative with Takashimadaira as its primary target area in Japan using wearable sensor data. A total of 500 community-dwelling older adults participated in the initiative, and wearable sensor data were obtained from 355 participants during the five-month observational period. The initiative combined wearable device distribution with event-information dissemination via the messaging platform. Daily step count and heart-rate indices were analyzed. Within-participant analyses showed significant increases in daily step counts in the information-provision groups, whereas the primary linear mixed-effects model showed no significant information-provision × time interaction for daily step count. Information provision was associated with differences in mean and minimum daily heart rate in cross-sectional analyses. Adherence and dropout also varied by residential area. Age, BMI, and exercise frequency were associated with several health-related indices, but these associations were not interpreted as evidence of wearable measurement validity. These findings demonstrate the potential utility of using wearable sensor data to evaluate longitudinal changes in health-related behavior and physiological signals in community-based DX initiatives, while also revealing important feasibility limitations, including substantial attrition (only 222 of 355 participants met the 14-day criterion; 90-day dropout 58.3%) and indicating that observed within-participant changes should not be interpreted as demonstrated intervention effects. Community context may also be relevant to sustained participation in such programs. Full article

Policosanol (sugarcane wax alcohol), phosphatidylserine, and Ginkgo biloba leaf extract are Ministry of Food and Drug Safety (MFDS), Republic of Korea-listed functional foods for health-promoting effects. Herein, a combination of policosanol, phosphatidylserine, and G. biloba leaf extract (hereafter PPG) was investigated against metabolic stress induced by a high-cholesterol, high-galactose (HCHG) diet in hyperlipidemic and hyperglycemic zebrafish. After 9 weeks of feeding, the zebrafish following the HCHG diet co-supplemented with PPG exhibited a significant 14.1% (p < 0.02) reduction in body weight and higher zebrafish survivability compared to the HCHG-supplemented group. Significantly reduced total cholesterol (TC, 1.2-fold), triglycerides (TG, 1.4-fold), low-density lipoprotein cholesterol (LDL-C, 1.3-fold), and glucose levels (1.2-fold), along with a 1.5-fold (p = 0.003) elevated high-density lipoprotein cholesterol (HDL-C), were observed in the PPG co-supplemented group relative to the HCHG group. In addition, the HCHG-elevated plasma malondialdehyde (MDA), diminished ferric ion reduction activity (FRA), and paraoxonase (PON)-like activity significantly (p < 0.001) reverted by 1.6-fold, 1.4-fold, and 1.6-fold, respectively, by co-supplementation with PPG. Consistently, the plasma from the PPG-supplemented group showed greater ability to prevent carboxymethyllysine (CML)-induced apoptotic death, altered heart rate, and developmental deformities in zebrafish embryos. Moreover, PPG exerted significant protective effects against HCHG-induced hepatomegaly and fatty liver, accompanied by marked inhibition of hepatic interleukin (IL)-6 and reactive oxygen species (ROS) generation. Consistently, PPG supplementation inhibits ROS generation and senescence in the liver, intestine, brain, kidney, testis and ovary and protects the organ damage caused by exposure to HCHG. Similarly, in intestinal tissue, HCHG-induced fibrosis and oxidative stress were mitigated by the co-supplementation of PPG. The findings indicate that PPG is an effective combination for preventing dyslipidemia, oxidative stress, inflammation, and multi-organ damage associated with HCHG-mediated metabolic stress in zebrafish. Full article

Water treatment plants are essential urban infrastructure with direct implications for public health and everyday life. Data-driven management has received growing attention in drinking water treatment, particularly for optimizing chemical dosing to improve operational efficiency, reduce costs, and ease operator workload. AI-based prediction of coagulant dosage has therefore become an active research topic. Existing studies, however, have focused mainly on model architecture, with less attention to data validity and cost control. In practice, many plants face data-quality problems, including inconsistent dosing records under similar water-quality conditions. Conventional data cleaning may also remove large portions of the dataset, which can weaken model reliability. This study proposes an artificial intelligence (AI) modeling framework for coagulation dosing that handles anomalous data, emphasizes data quality assurance, and combines cost-oriented feedforward prediction with feedback control. A genetic algorithm-optimized backpropagation (GA-BP) neural network was first evaluated on controlled laboratory data and full-scale plant data using the same core model architecture, allowing the effects of model configuration to be separated from those of data quality. Historical plant records were subsequently cleaned through expert-guided validation, approximate time-delay alignment, and turbidity-based classification of operating conditions. Settled-water turbidity was then used as a feedback signal to dynamically adjust subsequent coagulant dosage and assess the resulting chemical savings. Changes in the input structure produced only modest improvements in full-scale prediction performance (R² = 0.53–0.72). In contrast, data cleaning and process-based data organization markedly improved predictive performance, with R² values increasing to 0.927–0.969. Standalone AI models achieved only moderate dosage reductions, while their integration with real-time turbidity feedback provided the best cost-control performance. The model-based control strategy reduced average coagulant consumption by 10.37%, with a maximum reduction of 21.33% at a settled-water turbidity target of 1.9 nephelometric turbidity units (NTU). Across the evaluated feedback-control scenarios, manual dosing was up to 32.83% higher than the corresponding feedback-controlled dosage. Overall, AI models can fit coagulation-dosing data and predict coagulant dosage with sufficient accuracy, but data quality assurance remains the main factor determining model performance. Effective cost control also requires real-time turbidity-based feedback regulation rather than model outputs alone. Full article

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article

Accurate and robust state-of-health (SOH) estimation is essential for efficient battery management systems (BMS), especially in practical scenarios suffering from severe cell-to-cell inconsistencies and data distribution shifts. Although prevailing data-driven estimation methods can achieve satisfactory accuracy within specific domains, their generalization capability degrades drastically when applied to unseen battery cells. To fill this research gap, this paper develops an integrated physics-guided and domain-aware SOH estimation framework. The proposed framework combines redundancy-aware feature screening, cycle-aware soft covariance alignment specifically designed for physics-based health indicators, as well as monotonicity-constrained LightGBM regression embedded with split conformal uncertainty quantification. Experimental validations are conducted on the NASA B0005, B0006 and B0007 battery datasets under the leave-one-cell-out (LOCO) cross-cell evaluation strategy. Comparative results reveal that the presented method achieves prominent performance improvement on the most difficult B6 domain, where the root mean square error (RMSE) is reduced from 0.1068 to 0.0845 with a decline rate of 20.9%, and the coefficient of determination ($R^2$) rises from 0.2464 to 0.5286, while maintaining stable estimation accuracy on less challenging target domains. In terms of overall performance, the average RMSE across all tested cells drops from 0.0499 to 0.0425, corresponding to a 14.8% reduction, indicating improved cross-cell performance across the investigated cells. Furthermore, the adaptive alignment mechanism can be dynamically activated only when necessary, effectively avoiding redundant distortion of the original feature distribution. The research findings indicate that the integrated framework can alleviate cross-cell battery degradation discrepancies under the investigated conditions. The proposed strategy demonstrates promising potential for improving cross-cell SOH estimation under the investigated laboratory conditions; however, the uncertainty intervals are not fully calibrated under severe domain shift, and broader validation on larger and more heterogeneous battery datasets is required before practical deployment claims. Full article

Mastitis remains one of the most important infectious diseases affecting dairy cows, with substantial consequences for animal health, milk production, and farm profitability. In this study, we extend a nonlinear SIRS–P compartmental model for mastitis transmission in dairy cows by incorporating sensitivity analysis, optimal control, and cost-effectiveness analysis. The model consists of susceptible, infected, and recovered cow populations together with an environmental pathogen compartment, and accounts for direct cow-to-cow transmission, indirect transmission through environmental contamination, recovery, recurrence of infection, recruitment, and culling. A sensitivity analysis of the basic reproduction number $R_0$ is performed to identify the parameters that most strongly influence mastitis transmission. An optimal control problem is then formulated using time-dependent controls that reduce direct transmission, reduce environmental contamination, and enhance recovery. Pontryagin’s Maximum Principle is applied to derive the optimality system, which is solved numerically using the forward–backward sweep method. Seven intervention strategies are compared through numerical simulations and cost-effectiveness analysis using ACER and ICER. The results indicate that the fully integrated strategy $S_7$, which combines prevention and screening, environmental sanitation, and treatment, provides the largest overall disease reduction. However, Strategy $S_1$, based on direct transmission reduction, offers the most cost-effective allocation of resources for reducing the mastitis infection burden. Full article

Pesticide contamination of food and environmental matrices represents a potential risk to human health. This study investigated the toxicological effects of commercial glyphosate and dicamba-based herbicide formulations, individually and in combination, on human intestinal epithelial Caco-2 cells. Cells were exposed to a concentration range of 0.1–10,000 mg/L for 24, 48, and 72 h to determine IC₅₀ values. Subsequent assays were conducted using concentrations based on the lowest IC₅₀ obtained and in the limits established by the Environmental Protection Agency for drinking water. After 48 h of exposure, glyphosate at the highest tested concentration, as well as co-exposure to glyphosate and dicamba, induced significant cytotoxicity, modulation of oxidative stress parameters, and alterations in antioxidant defenses, accompanied by increased rates of apoptosis and necrosis. Dicamba exposure alone also resulted in elevated apoptotic and necrotic cell populations. A reduction in N-acetyl-β-D-glucosaminidase activity was observed across most tested concentrations, suggesting impaired inflammatory response capacity. This work identified alterations in Caco-2 that impair cellular homeostasis by cytotoxicity, alterations in oxidative stress, antioxidant response, inflammation, apoptosis and necrosis. Future studies investigating inflammatory pathways, genetic damage, and assays with in vivo and in silico models are important to elucidate the mechanism of the cellular damage caused by exposure to these herbicides. Full article

Background: In the wake of the COVID-19 pandemic, nursing students and educators are navigating an increasingly complex academic environment. The rigorous demands of nursing education, combined with diminished academic preparedness and growing mental health concerns, have heightened the need for effective wellness interventions. The SKY Happiness Retreat©, an evidence-based program incorporating breathwork, meditation, and mindfulness practices, offers a potential strategy to enhance student resilience and reduce stress. Purpose: This pilot study examined the impact of integrating the SKY Happiness Retreat© into a Bachelor of Science in Nursing (BSN) program. This study evaluated whether participation reduced perceived stress and improved students’ ability to manage stressful situations. Methods: A quasi-experimental pretest–post-test design was used with junior-level BSN students recruited through convenience sampling at a rural university. Participants completed a structured three-day retreat focused on breathwork, meditation, and mindfulness. Outcomes were measured using the Mood and Anxiety Symptom Questionnaire (Mini-MASQ), the Perceived Stress Scale (PSS), and the Brief Resilience Scale (BRS). Results: Perceived stress significantly decreased following the intervention (PSS: pre M = 18.50, SD = 4.583; post M = 17.30, SD = 5.841; t(43) = 2.072, p = 0.022). General distress also significantly improved (Mini-MASQ: pre M = 15.51, SD = 6.535; post M = 12.82, SD = 5.118; t(44) = 3.941, p < 0.001). No significant changes were observed in anxious arousal or anhedonic depression. BRS scores demonstrated a modest increase in resilience. Conclusions: The SKY Happiness Retreat© shows promise as an experiential learning strategy within undergraduate nursing education. Findings suggest participation was associated with reductions in perceived stress and general distress while promoting resilience. Integrating evidence-based wellness programs into prelicensure nursing curricula may better prepare students to manage academic and professional stress. Larger, longitudinal studies are needed to evaluate long-term effectiveness. Full article

Traditional benefit–cost analysis (BCA) summarizes the average net social benefit of a policy but often obscures how benefits and costs are distributed across groups. This is a major limitation in health policy settings, where decision-makers must assess both efficiency and equity. This article develops an integrated equity-weighted causal benefit–cost analysis (IEW-BCA) framework for equity-conscious policy evaluation. The framework links subgroup-specific causal effect estimates, explicit valuation functions, and equity weights within a single evaluation pipeline suitable for applied policy appraisal. It makes three contributions: first, it provides an implementable structure for combining causal heterogeneity with welfare-consistent valuation; second, it introduces a compound equity vector and associated weighting functions that extend income-based schemes and can be interpreted, under standard conditions, as a first-order approximation to a broad class of social welfare functions; and third, it develops equity-oriented summary tools, including a causal equity gradient and related dominance criteria, to improve transparency about distributional impacts. A stylized example illustrates implementation, and the discussion outlines implications for uncertainty, sensitivity analysis, and potential application in health economics, public health policy, and regulatory evaluation. Full article

High-intensity interval training (HIIT) is widely recognized as an effective strategy for improving cardiorespiratory fitness and metabolic health. Beyond these physiological benefits, growing evidence indicates that HIIT may also induce beneficial immunometabolic adaptations. A key exercise-responsive metabolite in this context is lactate, which is increasingly being recognized not as a metabolic waste product but as a bioactive signaling metabolite capable of coordinating metabolic, inflammatory, and immune processes. This narrative review examines current evidence suggesting a potential role for exercise-induced lactate in immune responses associated with HIIT. We summarize the molecular pathways through which lactate may interact with immune cells, including uptake via monocarboxylate transporters (MCT1/MCT4) and SLC5A12, receptor-dependent signaling through GPR81/HCAR1, and epigenetic regulation via histone lactylation. We further discuss the cell-specific effects of lactate on macrophages, dendritic cells, neutrophils, and T lymphocytes, highlighting how these mechanisms may influence immune-cell metabolism, inflammatory regulation, and functional remodeling. A central concept emerging from the current literature is that the biological actions of lactate are highly dependent on the kinetics, duration, and physiological context of exposure. Unlike pathological lactate elevations observed in conditions such as cancer, sepsis, or mitochondrial myopathies—the latter potentially involving an exaggerated lactate response during exercise due to impaired oxidative metabolism—HIIT generates transient systemic lactate elevations as part of a coordinated neuroendocrine and metabolic response. When combined with adequate recovery, these repeated metabolic perturbations may promote hormetic adaptations characterized by improved inflammatory regulation, enhanced immune resilience, and more efficient immunometabolic homeostasis. Conversely, excessive training loads or inadequate recovery may shift these responses toward maladaptive immune stress. Overall, current evidence suggests a paradigm shift in exercise immunology in which lactate should be regarded as one component of an integrated immunometabolic signaling network rather than simply as a marker of anaerobic metabolism. Future mechanistic studies integrating lactate kinetics, immune-cell phenotyping, transporter expression, and lactate-dependent post-translational modifications are needed to clarify the extent to which lactate may contribute to exercise-induced immune remodeling and to guide the development of immunologically informed HIIT protocols. Full article

Background/Objectives: Illness coherence has been associated with treatment engagement and adherence, but its relationship with exercise adherence and patient-reported outcomes in chronic spinal pain remains unclear. This study examined whether unsupervised exercise adherence mediates the association between baseline illness coherence and patient-reported outcomes and whether this indirect association differs according to the type of intervention received. Methods: A theory-driven secondary analysis was conducted using data from a randomized controlled trial of adults with chronic spinal pain receiving either exercise-only or combined exercise plus diaphragmatic breathing intervention (initially randomized = 183; n = 57/group). Measures included Revised Illness Perception Questionnaire (IPQ-R), Brief Pain Inventory–Short Form (BPI-SF), Hospital Anxiety and Depression Scale (HADS), World Health Organization Quality of Life Instrument, Brief Version (WHOQOL-BREF), and exercise logs. Moderated mediation analyses examined whether the intervention group moderated the indirect associations between baseline illness coherence and patient-reported outcomes through unsupervised exercise adherence. The parent trial was retrospectively registered at ClinicalTrials.gov (NCT07539116) on 12 April 2026. Results: Mean age was 46.95 ± 14.54 and 46.32 ± 15.97 years, and women comprised 40.4% and 47.4% of the exercise-only and combined groups, respectively. Baseline illness coherence was positively associated with unsupervised exercise adherence in the exercise-only group (b = 71.04, p = 0.018). Significant conditional indirect effects of illness coherence on all patient-reported outcomes through unsupervised exercise adherence were observed in the exercise-only group (b = −1.55 to 1.32, ps < 0.05), but not in the combined intervention group (b = −0.59 to 0.32, ps > 0.05). Indices of moderated mediation were significant across all outcomes (all ps < 0.05). No intervention-related adverse events were reported. Conclusions: Diaphragmatic breathing may reduce the relative contribution of illness-related cognitive representations on patient-reported outcomes by introducing additional self-regulatory pathways through which clinical improvement is achieved. Full article

The intestinal barrier plays a critical role in maintaining gastrointestinal health and nutrient utilization in yaks. This study investigated the effects of dietary RPG level and RPT supplementation on colonic barrier function and microbial composition in yaks. Twenty-eight healthy male yaks (3 years old; 192.7 ± 4.52 kg) were assigned to a 2 × 2 factorial design with two dietary rumen-protected glucose (RPG) levels (1.0% or 3.0% of dietary DM) and two rumen-protected taurine (RPT) supplementation levels (5 or 20 g/animal/day) for 63 days. High-level RPG impaired colonic physical barrier function by reducing tight junction protein expression and microbial diversity, whereas high-level RPT mainly compromised chemical and immune barrier function by decreasing diamine oxidase activity, mucin-2, and secretory immunoglobulin A, accompanied by alterations in the colonic microbial community. Significant interactions between RPG and RPT were observed for several barrier- and microbiota-related indices. Overall, the effects of RPG and RPT on colonic health were dose-dependent, and moderate supplementation, particularly the combination of 1.0% dietary RPG and 5 g/day RPT, was the most effective in maintaining colonic barrier integrity and microbial homeostasis in yaks. Full article

Taekwondo is a high-intensity, intermittent, weight-category combat sport requiring repeated explosive actions, rapid decision-making, and effective recovery between bouts. These demands, combined with congested competition schedules and weight-management requirements, make nutrition important for both performance and athlete health. This narrative review provides an evidence-informed synthesis of nutritional strategies relevant to taekwondo performance, recovery, and weight management by integrating available taekwondo-specific evidence with findings from comparable combat sports and established sports nutrition guidelines. Particular attention is given to energy and carbohydrate availability, protein intake, hydration and electrolyte balance, selected ergogenic aids, nutritional periodization, and safe weight management. The review also proposes an applied integrative framework illustrating how nutrition may influence interacting physiological, neuromuscular, cognitive, and recovery-related processes. A key limitation is the scarcity of taekwondo-specific intervention studies, meaning that several practical recommendations are necessarily extrapolated from comparable combat sports and broader athletic populations. From an applied perspective, nutritional priorities should include maintaining adequate energy and carbohydrate availability, supporting recovery through appropriate protein and fluid–electrolyte intake, and adopting gradual, individualized weight-management strategies that minimize dehydration, low energy availability, and Relative Energy Deficiency in Sport (RED-S) risk. Overall, nutritional strategies in taekwondo should be individualized and aligned with training load, competition demands, recovery needs, and weight-management goals. Further taekwondo-specific experimental studies are needed to refine these recommendations and strengthen sport-specific nutritional guidance. Full article

Background/Objectives: Traumatic intracranial hemorrhage (tICH) in older adults following low-energy falls (LEF) represents a common yet diagnostically challenging condition in the emergency department (ED), where predicting injury prior to computed tomography (CT) remains difficult. Machine learning (ML) has been proposed to support CT decision-making, but its feasibility using routinely available pre-CT clinical variables in this specific population remains unclear. This study presents a systematic exploratory benchmarking of ML pipeline configurations for pre-CT tICH prediction in a well-defined retrospective cohort of older emergency patients following LEF. Methods: We performed a secondary analysis from a retrospective observational bicentric study from two university hospital EDs, including 2250 patients aged ≥65 years presenting after an LEF and undergoing cranial CT. Clinical data were extracted manually from electronic health records (EHRs). Eighteen pre-CT clinical features retrieved from electronic health records were selected based on routine availability and ≤10% missingness. Overall, 1224 valid ML pipeline configurations, combining nine classification algorithms, six imputation strategies, four class-balancing approaches, and optional hyperparameter tuning, were evaluated using 10-fold stratified cross-validation on a training set. The 20 highest-ranked configurations by cross-validation AUC were then assessed on a previously inspected exploratory hold-out test set (n = 563); training-derived rule-out operating points were evaluable for 17 of these 20, as three tuned SVM configurations lacked stored out-of-fold predictions. Results: tICH prevalence was 7.0% (n = 158). Across the 20 highest-ranked configurations, hold-out AUC ranged from 0.517 to 0.585, with Matthews correlation coefficient near zero and balanced accuracy of approximately 50% throughout, indicating differences in operating point rather than in discriminative ability. Some of these top-ranked pipelines reached higher cross-validation AUC (up to 0.679) but detected no cases at the default 0.5 threshold—an effect of the decision threshold under class imbalance rather than of the models’ rank-order discrimination, which was itself limited (hold-out AUC of 0.517–0.585). Conclusions: Despite comprehensive exploratory benchmarking across 1224 ML pipelines, routinely available pre-CT clinical features did not provide sufficient discriminatory signal to develop a clinically useful tICH prediction model in this cohort of CT-imaged older adults following LEF. These findings indicate that none of the evaluated configurations produced clinically adequate performance; this near-chance result persisted across all pipelines and most plausibly reflects a combination of limited feature signal, low outcome prevalence, and a sample size below the level required for reliable model development at this event rate. Future studies should target substantially larger prospective multicenter cohorts and evaluate additional feature domains, including structured clinical examination findings, point-of-care biomarkers, and imaging features. Full article

Increasing fertilizer costs, climate-related stresses, and soil degradation caused by the prolonged use of chemical fertilizers threaten the sustainability of agricultural production. Organic soil amendments offer a promising approach to restoring soil health while reducing dependence on synthetic inputs. This study evaluated the combined effects of biochar and organic fertilizer on soil health and soybean (Glycine max L.) productivity under acidic soil conditions. During the 2024 growing season, a greenhouse pot experiment was conducted using a completely randomized design (CRD) comprising seven treatments. Each treatment was replicated three times, resulting in a total of 21 pots. The treatments consisted of different combinations of biochar (B) and organic fertilizer (F), applied at rates of grams per 10.5 kg of soil: control (B0F0), B35F70, B35F105, B35F140, B70F70, B70F105, and B70F140. Treatment means were compared using the Least Significant Difference (LSD) test at p < 0.05. The results showed that the highest soil pH value (5.41) was recorded under the B35F70 treatment at 45 days after amendment of the acidic soil. Application of the B35F140 treatment resulted in a significant increase (p < 0.05) in electrical conductivity (0.23 mS cm⁻¹) compared with the control. Soil organic matter and available phosphorus reached their highest values under B70F140, at 5.25% and 13.87 mg kg⁻¹, respectively, and were significantly greater than those in the control treatment. Soil available iron (Fe) and manganese (Mn) concentrations also increased significantly (p < 0.05) compared with the control, with the B35F70 and B70F70 treatments resulting in the highest Fe (371.29 mg kg⁻¹) and Mn (37.77 mg kg⁻¹) concentrations, respectively. At 100 days after amendment of the reddish-brown acidic soil, exploratory Pearson correlation analyses were conducted to examine relationships between soil health indicators and soybean performance. Soil available phosphorus and potassium exhibited positive associations with soil pH (r = 0.69 and r = 0.65, respectively; p < 0.01). Soybean growth traits, including plant height and number of leaves, were positively associated with seed yield (r = 0.56 and r = 0.77, respectively; p < 0.01). Furthermore, seed yield was positively correlated with SPAD values (r = 0.80, p < 0.01), soil pH (r = 0.56, p < 0.01), available K (r = 0.68, p < 0.01), and Mg (r = 0.47, p < 0.05). Principal component analysis (PCA) further demonstrated clear treatment clustering and consistent positive relationships among soil properties, plant growth traits, and soybean yield variables. The control treatment was clearly separated from all biochar-organic fertilizer treatments along PC1. Among all treatments, B35F140 (3.33 g biochar kg⁻¹ soil + 13.33 g organic fertilizer kg⁻¹ soil) showed the strongest positive association with soil health and plant growth and produced the highest soybean seed yield (10.77 g plant⁻¹). Overall, the combined use of biochar and organic fertilizer improved soil health, soybean growth, and yield, demonstrating its potential as a sustainable strategy for enhancing soybean productivity under acidic soil conditions. Full article