Search Results (262)

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications. Full article

This study investigates the feasibility of absolute decoupling—where economies expand while CO₂ (Carbon Dioxide) emissions decline in absolute terms—by identifying its key macro–energy drivers across 79 countries (2000–2025). We construct a comprehensive panel of energy-system indicators and estimate the probability of decoupling using two complementary classifiers: a penalized logistic regression and a gradient-boosted decision tree model (GBM). The non-parametric GBM significantly outperforms the linear baseline (ROC–AUC ~0.80 vs. 0.67), revealing complex non-linearities in the transition process. Explainable AI analysis (SHAP) demonstrates that decoupling is not driven by GDP growth rates alone, but primarily by sharp reductions in energy intensity and the active displacement of fossil fuels. Crucially, our results indicate that increasing renewable capacity is insufficient for absolute decoupling if the fossil fuel share does not simultaneously decline. These findings challenge passive “green growth” narratives, suggesting that current policies are inadequate; achieving climate targets requires targeted mechanisms for active fossil fuel phase-out rather than merely relying on renewable additions or economic modernization. Full article

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique. Full article

Objective: In the context of an aging population, the prevention and control of cognitive impairment is a key public health priority. This study aims to investigate the association between proactive health behaviors and the risk of AD8 screening positivity in older adults in China, providing an empirical basis for developing targeted intervention strategies. Methods: Based on health behavior data from 1110 older adults in China, the chi-square test was used to analyze the differences in proactive health behaviors (such as limiting salt and alcohol intake, smoking cessation, and vaccination) between the low-risk and high-risk groups for AD8 screening. Factor analysis was used to extract the main factors of proactive health behaviors. Firth penalized logistic regression models were used to analyze the impact of the main factors and sociodemographic factors on the risk of cognitive impairment. Results: The chi-square test showed that there were significant differences between the two groups in salt restriction behavior (χ² = 18.063, p < 0.01) and vaccination (χ² = 29.674, p < 0.01), with a higher proportion of salt restriction (34.7%) and vaccination rates (80.4%) in the low-risk group. Factor analysis extracted four main factors (psychological–social support, information–behavior execution, technology–environment promotion, and addictive behavior control), with a cumulative variance contribution rate of 58.45%. Among them, psychological–social support (31.42% explained variance) and information–behavior execution (28.04%) had the strongest explanatory power. Firth penalized logistic regression showed that psychological–social support (Firth-corrected OR = 0.072, 95% CI: 0.035–0.148, p < 0.01) and information–behavior execution (Firth-corrected OR = 0.008, 95% CI: 0.003–0.021, p < 0.01) had significant protective effects on AD8 screening positivity (standardized OR values indicated that each one-standard-deviation increase in these two factors reduced screening-positive risk by 39% and 53%, respectively), and the risk increased by 21.7% for every 5-year increase in age (OR = 1.217, p = 0.001). Technology–environment promotion (OR = 0.417, 95% CI: 0.250–0.691, p = 0.001) and addictive behavior control (OR = 0.709, 95% CI: 0.490–1.026, p = 0.068) showed no significant protective effects. Sensitivity analysis confirmed the robustness of the four-factor structure and core conclusions. Conclusions: Among proactive health behaviors, psychological–social support and information–behavior execution are key protective factors in reducing the risk of AD8 screening positivity in older adults, and age is an important influencing factor. Strengthening psychological support and optimizing access to health information and behavior execution can serve as core strategies for cognitive impairment prevention and control, providing empirical support for the formulation of health policies for older adults. Full article

Background/Objectives: Pathological complete response (pCR) is an established surrogate marker of neoadjuvant chemotherapy (NACT) efficacy in breast cancer; however, reliable predictors of pCR remain limited. Immune–inflammation- and nutrition-based biomarkers derived from routine blood tests may offer accessible tools for early assessments of treatment response. This study aimed to evaluate both baseline values and dynamic (Δ) changes in multiple immune–nutritional indices to determine their predictive performance with regard topCR. Methods: A retrospective analysis was conducted on 236 early breast cancer patients who received neoadjuvant chemotherapy. Pre-treatment (B), post-treatment (A), and Δ values were calculated for the prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI), hemoglobin–albumin–lymphocyte–platelet (HALP) score, systemic inflammation response index (SIRI), pan-immune–inflammation value (PIIV), global immune–nutrition-information index (GINI), nutritional risk index (NRI), and related biomarkers. Associations with pCR were examined using chi-square testing and univariate logistic regression, and diagnostic performance was assessed through receiver operating characteristic (ROC) analysis. Results: pCR was achieved in 116 patients (49.2%). Logistic regression identified the NRI (OR = 2.336), ΔGINI (OR = 2.323), ALI (OR = 1.318), PNI (OR = 1.365), HALP score (OR = 1.217), ΔSIRI (OR = 2.207), and ΔPIIV (OR = 2.001) as significant predictors. ROC analysis showed that the NRI (AUC = 0.840) and ΔGINI (AUC = 0.807) were the strongest discriminators of pCR. In aLASSO (Least Absolute Shrinkage and Selection Operator)-penalized logistic regression with 10-fold cross-validation, the NRI and ΔGINI emerged as independent predictors of pCR (OR = 1.28 and OR = 1.23, respectively), showing acceptable calibration particularly in the moderate-to-high probability range. Conclusions: Both baseline and Δ immune–nutritional biomarkers predict pCR following NACT in breast cancer. The NRI and ΔGINI demonstrated the best diagnostic performance, whereas ΔSIRI and ΔPIIV also showed meaningful associations. Easily obtainable, low-cost indices—particularly Δ markers—may support the early identification of responders and facilitate more personalized therapeutic decision-making in breast cancer management. Full article

The extensive use of sulfathiazole in poultry farming has raised growing concerns regarding its residues in poultry-derived products, posing risks to human health and food safety. To overcome the limitations of conventional detection methods and address the analytical challenges posed by inherent complexity of chicken blood matrix for the detection of sulfathiazole residues in chicken blood, a rapid and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed for detecting sulfathiazole residues in chicken blood. Four colloidal substrates, i.e., gold colloid A, gold colloid B, gold colloid C, and silver colloids, were synthesized and evaluated for their SERS enhancement capabilities. Key parameters, including electrolyte type (NaCl solution), colloidal substrate type (gold colloid A), volume of gold colloid A (550 μL), volume of NaCl solution (60 μL), and adsorption time (14 min), were systematically optimized to maximize SERS intensities at 1157 cm⁻¹. Furthermore, a genetic algorithm-support vector regression (GA-SVR) model integrated with adaptive iteratively reweighted penalized least squares (air-PLS) and multiplicative scatter correction (MSC) preprocessing demonstrated superior predictive performance with a prediction set coefficient of determination (R²_p) value of 0.9278 and a root mean square error of prediction (RMSEP) of 3.1552. The proposed method demonstrated high specificity, minimal matrix interference, and robustness, making it suitable for reliable detection of sulfathiazole residues in chicken blood and compliant with global food safety requirements. Full article

Background/Objectives: In low-resource settings, diagnostic delays and limited specialist access worsen health inequalities, making hypertensive disorders of pregnancy (HDPs) defined by new-onset blood pressure ≥ 140/90 mmHg after 20 weeks of gestation, with or without proteinuria, a major cause of maternal morbidity and mortality. This study evaluated the diagnostic effectiveness of a rural-applicable clinical model for detecting HDPs in a real-world population from Hospital General San Felipe (Tegucigalpa, Honduras). Methods: A cross-sectional diagnostic accuracy study was conducted on 147 consecutive pregnant women in February 2025. Clinical documentation from the initial appointment defined HDP. We modeled HDP risk using penalized logistic regression and common factors such maternal age, gestational age, blood pressure, BMI, primary symptoms, semi-quantitative proteinuria, and medical history. Median imputation was utilized for missing numbers and stratified five-fold cross-validation assessed performance. We assessed AUROC, AUPRC, Brier score, calibration, and operational utility at a data-driven threshold. Results: Of patients, 27.9% (41/147) had HDP. The model had an AUROC of 0.614, AUPRC of 0.461 (cross-validation averages), and Brier score of 0.253. The threshold with the highest F1-score (0.474) had a sensitivity of 0.561, specificity of 0.679, positive predictive value of 0.404, and negative predictive value of 0.800. HDP had higher meaning systolic/diastolic/mean arterial pressure (130.7/82.9/98.9 vs. 120.5/76.1/90.9 mmHg) and ordinal proteinuria (0.59 vs. 0.36 units). Conclusions: The model had moderate but clinically meaningful discriminative performance using low-cost, commonly obtained variables, excellent calibration, and a good negative predictive value for first exclusion. These findings suggest modification of predictors, a larger sample size, and clinical usefulness assessment using decision curves and process outcomes, including quick referral and prophylaxis. This approach aligns with contemporary developments in the 2023–2025 European Society of Cardiology (ESC) and 2024 American Heart Association (AHA) guidelines, which emphasize earlier identification and risk-stratified management of hypertensive disorders during pregnancy as a cornerstone of women’s cardiovascular health. Full article

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but its sustainability benefits depend on reliable, constraint-aware nutrient dosing under delayed inflow effects. Aquaponics involves coupling hydroponics with aquaculture but is difficult to control because the greenhouse/crop state at the current time step ($t$) must anticipate water-quality changes that arrive at the next time step ($t+1$), under hard EC–pH and dose constraints. We propose the Analysis System for Nutrient Requirements in Hydroponics (ASNRH), a two-module, constraint-aware framework that directly regresses next-step elemental supplementation (N, P, K; mg·L⁻¹). First, the Fish-farm By-product Prediction Module (FBPM) uses a lightweight GRU forecaster to predict inflow chemistry at $t+1$ (e.g., NH₄⁺/NO₂⁻/NO₃⁻, alkalinity) from standard aquaculture sensors. Second, the Nutrient Requirement Prediction Module (NRPM) encodes the current hydroponic and crop state at t in parallel with the FBPM inflow at $t+1$ via a dual-branch architecture and fuses both representations to produce non-negative dose recommendations while penalizing forecasted EC/pH violations and excessive actuation volatility. The data pipeline assumes low-cost greenhouse and aquaculture sensors with chronological, leakage-free splits. A protocol-first simulation evaluates ASNRH against time-series and rule-based baselines using accuracy metrics (MAE/RMSE/R²), EC/pH violation rates, and robustness under missingness/noise; ablations isolate the contributions of the inflow branch, constraint-aware losses, and lightweight physics priors. The framework targets deployability in decoupled or coupled aquaponics by structurally resolving $t$ vs. $t+1$ asynchrony and internalizing domain constraints during learning; procedures are specified to support reproducibility and subsequent field trials. By operationalizing anticipatory dosing from reused aquaculture byproducts under EC/pH feasibility constraints, ASNRH is designed to support sustainability goals such as reduced nutrient wastage and fewer corrective water exchanges in coupled or decoupled aquaponics. Full article

Background: Travel-associated melioidosis, caused by Burkholderia pseudomallei, is increasingly reported in non-endemic countries due to rising global travel. Understanding demographic, clinical, and outcome patterns of imported cases is important to improve recognition and management in settings where melioidosis is uncommon. Methods: We systematically searched PubMed, Embase, and Scopus (last search: 24 September 2025) for case reports and case series of melioidosis diagnosed outside endemic regions and linked to travel exposure. Data were extracted on demographics, comorbidities, clinical manifestations, and outcomes. We performed descriptive analyses, subgroup analyses, and Firth’s penalized logistic regression to explore predictors of death. The protocol was registered in PROSPERO (CRD420251154559). Results: A total of 104 studies, encompassing 143 individual cases, were included. Most diagnoses occurred in non-endemic, high-income countries, especially the Netherlands (21%), France (10%), the United States (9%), and South Korea (7%). Infections were predominantly acquired in Southeast Asia, particularly Thailand (39%). The mean patient age was 50.6 years, with a male predominance (78%). Diabetes mellitus was the most frequent comorbidity (28%). Clinical presentations included pulmonary (33%), sepsis (27%), cutaneous (13%), abdominal (4%), and osteoarticular disease (1%). Overall mortality was 12.6% and relapse occurred in 7%. In penalized regression analyses, no baseline characteristic was statistically significantly associated with mortality; septic presentation showed an elevated point estimate for odds of death, but with imprecise estimates. Conclusions: Travel-associated melioidosis is a rare but clinically significant imported infection. Most cases followed exposure in Southeast Asia, and pulmonary disease and sepsis were the most frequent presentations. Mortality remained substantial (12.6%), and relapse was reported in 7%, underscoring the need for early recognition, appropriate therapy, and follow-up in non-endemic settings. Full article

Background: Psychological distress is common in hemodialysis patients and is linked to worse clinical outcomes and lower quality of life. Nutritional and inflammatory disturbances may impact emotional well-being. Gender likely acts as a biological and psychosocial modifier. This study examined the link between depression, anxiety, and stress in hemodialysis patients and a broad range of biochemical markers, focusing on gender as a main factor. Methods: A cross-sectional study included 54 adults on maintenance hemodialysis at a hospital in Madrid, Spain. Emotional distress was measured using the DASS-21. Predialysis biochemical markers assessed were β₂-microglobulin, albumin, hemoglobin, hematocrit, phosphorus, potassium, iron, calcium, and vitamin D. Statistical analyses included Spearman correlations, HC3-robust regressions with Gender × Biomarker interactions, false discovery rate correction (q = 0.10), penalized regressions (ridge/LASSO), partial least squares structural equation modeling (PLS-SEM), and mixed-cluster analysis. Results: Women reported higher depression, anxiety, and stress, and had lower albumin, calcium, and vitamin D (p < 0.05). Depression was independently linked to female gender, lower calcium, and the Gender × β₂-microglobulin interaction (adjusted R² = 0.30). In PLS-SEM analysis, a latent global psychological distress measure was directly related to β₂-microglobulin and inversely related to albumin and calcium (R² = 0.47). Nutritional markers partly mediated the gender–distress link. Cluster analysis found three biopsychosocial profiles: metabolically balanced, catabolic–emotional, and resilient–compensated. Conclusions: Gender shapes the relationships among inflammation, nutrition, and psychological distress in hemodialysis. Including gender-sensitive emotional and nutritional assessments in nephrology nursing could foster more personalized and practical care. Findings highlight the value of gender-aware psycho-nutritional screening in dialysis. Full article

This paper explores the impact of sustainability practices (ESG and greenwashing) on profitability, with a special focus on crises, in particular COVID-19. We analyze the post-crisis profitability of sustainable firms, distinguishing between country groups (developed/developing and high voice and accountability/low voice and accountability). We run fixed effects difference-in-differences regressions on a dataset comprising 3832 to 14,652 firm-year observations from 47 stock exchanges. Evidence suggests that firms with a higher ESG during COVID-19 achieved higher profitability afterwards. Environmental score (E) contributed the most to profitability. Although greenwashing improved post-crisis operational performance, its effect on market value is mixed. Countries exhibited different dynamics regarding the sustainability–profitability nexus: firms in developed countries and countries with greater voice and accountability achieved higher post-crisis profitability. In the post-crisis recovery period, this pattern reversed (for ESG) or disappeared (for greenwashing). Overall, companies investing in ESG (E, S, G) during COVID-19 gained substantial benefits, but those engaging in greenwashing were not penalized much, especially those in developed countries and countries with greater voice and accountability. These findings show how institutional and stakeholder-driven pressures can play a critical role in the sustainability–profitability relationship, engendering important implications for managers and policymakers. Full article

Milk fatty acid (FA) synthesis and enteric methanogenesis share common biochemical pathways related to rumen fermentation patterns and microbial volatile FA production. The FA profile of milk is known to correlate with methane (CH₄) emissions; thus, FA profiling has been proposed as an indirect method to predict CH₄ emissions from dairy cattle. This study aimed to (1) investigate the milk FA profiles of Holstein cows to identify candidate biomarkers for predicting CH₄ output (g/d), CH₄ yield (g/kg dry matter intake), and CH₄ intensity (g/kg energy-corrected milk), and (2) develop and compare regression models predicting CH₄ emissions. Forty-eight cows, fed industry standard diets, were enrolled in an exploratory trial. Milk samples and CH₄ measurements were collected thrice per day, and intake was recorded daily. Milk lipids were extracted, transesterified, and subsequently analyzed via gas–liquid chromatography. Three penalized regression models were compared for predicting CH₄ emission metrics using milk FAs and management variables. Methane emission metrics corelated positively with short- and medium-chain FAs, polyunsaturated FAs, and branched-chain FAs, while monounsaturated FAs correlated negatively. Notably, this study observed novel correlations between 11-cyclohexyl-11:0; and 20:3 c5,c8,c11 and CH₄ metrics (|r| = 0.58–0.79). Across all CH₄ metrics, the models demonstrated high predictive accuracy (R² = 0.71–0.87; concordance correlation coefficient = 0.83–0.93). The findings of this study indicate that milk FA profiling may be an effective method to detect CH₄ emissions from cows fed industry standard diets and highlight the need for further refinement of prediction models. Full article

This study examines the underrepresentation of women in STEMM (Science, Technology, Engineering, Mathematics, and Medicine) within Peruvian public universities and identifies factors associated with women’s program choice. A cross-sectional survey was administered to first-term students across three public institutions spanning Peru’s Highlands, Coast, and Amazon regions. Data from 1142 students (145 women) were used for descriptive analysis of segregation, while an inferential sample (N = 152; 76 STEMM, 76 non-STEMM) was used for modeling. The instrument was an adapted “University Students’ Questionnaire on STEM Studies in Higher Education (QSTEMHE)” (Cronbach’s α = 0.89). Descriptive statistics and a penalized (Firth) binary logistic regression were used to evaluate sociodemographic, contextual/experiential, and motivational predictors of enrolling in a STEMM major. The cross-sectional design limits causal inference, and perception data are subject to self-report biases. Women accounted for 12.7% of STEMM enrolment overall, with pronounced horizontal segregation: engineering programs frequently recorded critically low female participation (≈3–5% in Civil, Mechanical, and Computer Engineering), whereas Medicine and Sanitary Engineering showed comparatively higher representation (27–38%). Perception data indicated that STEMM students more strongly rejected gender–ability stereotypes than non-STEMM peers, although a substantial proportion still reported constraining gender expectations and rigid household roles. In the penalized regression, Prior Interest in STEM (OR = 7.76; p = 0.018) and Motivation: Opportunities (OR = 2.24; p = 0.0001) significantly increased the probability of choosing STEMM. Crucially, Ethnicity emerged as a significant barrier: identifying as ‘Quechua’ (OR = 0.19; p = 0.0004) or ‘Other(s)’ (OR = 0.16; p = 0.011) significantly decreased this likelihood. Age, area of residence, and Motivation: Altruism was not significant. Findings support early, gender-responsive career guidance, mentoring, addressing intersectional ethnic barriers, and targeted financial aid to strengthen women’s participation and retention in STEMM. Full article

Background/Objectives: Asthma is a heterogeneous inflammatory airway disease. Periostin, a matricellular protein induced by interleukin-13, contributes to airway inflammation and remodeling. This study evaluated serum periostin as a diagnostic biomarker and explored multidimensional phenotypes in adult asthma. Methods: A cross-sectional study included 76 adults, with 25 healthy controls, 25 moderate, and 26 severe asthma patients, classified per Global Initiative for Asthma (GINA)-2020 guidelines. Serum periostin was measured using an enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, Firth-penalized logistic regression, bootstrap calibration (1000 resamples), decision curve analysis (DCA), and gradient boosting machine (GBM) validation. Principal component analysis (PCA) followed by k-means clustering identified distinct phenotypes based on clinical, functional, and inflammatory variables. Results: Asthma patients had higher serum periostin than controls (median 52.9 vs. 32.5 pg/mL; p < 0.01), with excellent diagnostic accuracy (AUC = 0.987; sensitivity = 94.1%, specificity = 100%). Firth regression identified periostin as the only independent predictor of asthma diagnosis (β = 0.387; OR = 1.47; 95% CI 1.23–2.08; p < 0.001). Calibration showed minimal error (MAE = 0.042) and DCA demonstrated clear net benefit. GBM confirmed periostin as the dominant diagnostic predictor. PCA revealed three clusters: Cluster 1: younger, lower periostin, preserved lung function, good symptom control; Cluster 2: intermediate periostin, greater airflow limitation, poorer control; and Cluster 3: highest periostin, elevated systemic inflammation (NLR, PLR, SII), with moderate functional impairment. Conclusions: Serum periostin is a reliable diagnostic biomarker for asthma. Multidimensional clustering highlights clinically relevant phenotypes linked to periostin, inflammatory burden, and lung function, supporting its role in personalized asthma management. Full article

Background: Diabetic neuropathy affects almost half of diabetic patients, yet the relative contributions of metabolic, vascular and clinical factors remain controversial. We aimed to investigate which risk factors are more associated with reduced corneal nerve fiber length (CNFL). Methods: This is a cross-sectional study of 30 patients with type 2 diabetes. We assessed metabolic parameters (HbA1c, lipids), vascular measurements (Doppler ultrasonography of carotid and ophthalmic arteries, central vessel density measured by optical coherence tomography angiography), and corneal epithelial thickness. We explored the data using network analysis, then applied penalized mixed-effect regression (in which $\beta$ represents the standardized coefficients with mean 0 and unit standard deviation), followed by generalized additive models and polynomial transformations. Results: Penalized regression identified vascular parameters as dominant predictors: carotid plaques ($\beta$ = −0.609) and intima-media thickness ($\beta$ = −0.574) showed the strongest associations with CNFL. Traditional metabolic markers including HbA1c failed to meet selection thresholds. Bifurcation velocity ($\beta$ = −0.313) and corneal sensitivity measures ($\beta$ = 0.278–0.135) were also significant. The non-linear modeling showed complex vascular–structural interactions. Conclusions: Vascular compromise, particularly carotid disease, had the highest association with CNFL in our cohort. Thus, our study reports a higher effect of vascular parameters than HbA1c in patients with a longer history of diabetes. This may reflect the progression of diabetic complications, where initial metabolic insults are followed by vascular pathology as the primary driver of end-organ damage. Our findings highlight the need for carotid artery screening in diabetic patients for a better estimation of the neuropathy risk. Full article