Search Results (1,566)

Microfluidic paper-based analytical devices (µPADs) convert ordinary cellulose into an active analytical platform where capillary gradients shape transport, surface chemistry guides recognition, and embedded electrodes or optical probes translate biochemical events into readable signals. Progress in fabrication—from wax and stencil barriers to laser-defined grooves, inkjet-printed conductive lattices, and 3D-structured multilayers—has expanded reaction capacity while preserving portability. Detection strategies span colorimetric fields that respond within porous fibers, fluorescence and ratiometric architectures tuned for low abundance biomarkers, and electrochemical interfaces resilient to turbidity, salinity, and biological noise. Applications now include diagnosing human body fluids, checking food safety, monitoring the environment, and testing for pesticides and illegal drugs, often in places with limited resources. Researchers are now using learning algorithms to read minute gradients or currents imperceptible to the human eye, effectively enhancing and assisting the measurement process. This perspective article focuses on the newest advancements in the design, fabrication, material selection, testing methods, and applications of µPADs, and it explains how they work, where they can be used, and what their future might hold. Full article

Background/Objectives: Recessive dystrophic epidermolysis bullosa (RDEB) is a severe congenital genodermatosis characterized by skin and mucosa fragility, chronic inflammation, recurrent infections and high nutritional demands due to increased metabolism and epithelial barrier-related losses, placing patients at risk of zinc deficiency. We aimed to investigate the clinical relevance and biochemical determinants of zinc deficiency as a potentially modifiable contributor to disease burden in RDEB. Methods: In this cross-sectional study (n = 84), serum zinc levels were analyzed in association with sex, age, disease severity, percentage of body surface area (BSA) affected, inflammatory markers, infection burden, and common clinical complications including anemia and growth impairment. Results: Zinc deficiency, defined as levels below 670 µg/L, was identified in 35% of patients and became more frequent after age 5 and during adulthood, particularly among those with more severe disease. Deficiency was strongly associated with anemia, inflammation, infection burden, growth impairment, and extensive skin involvement. A revised cutoff of 780 µg/L is proposed, showing improved diagnostic performance for identifying patients at risk of systemic complications, and offering a more suitable threshold for starting preventive supplementation. Multivariate logistic modeling confirmed that low serum zinc independently predicted anemia risk, alongside transferrin saturation and C- reactive protein levels. Serum albumin was identified as the strongest determinant of zinc levels, partially mediating the effects of inflammation and skin involvement. Conclusions: These findings identify serum zinc as a clinically relevant marker of nutritional status and complication burden in RDEB. While no causal or therapeutic effects can be inferred from this cross-sectional study, the strong and biologically plausible associations observed suggest a rationale for systematic monitoring and correction of zinc deficiency as part of comprehensive supportive care, and warrant prospective studies to assess clinical benefit. Full article

Artificial intelligence (AI) and machine learning (ML) are reshaping cancer research and care. In pediatric oncology, early evidence—most robust in imaging—suggests value for diagnosis, risk stratification, and assessment of treatment response. Pediatric endocrine tumors are rare and heterogeneous, including intra- and extra-adrenal paraganglioma (PGL), adrenocortical tumors (ACT), differentiated and medullary thyroid carcinoma (DTC/MTC), and gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN). Here, we provide a pediatric-first, entity-structured synthesis of AI/ML applications in endocrine tumors, paired with a methods-for-clinicians primer and a pediatric endocrine tumor guardrails checklist mapped to contemporary reporting/evaluation standards. We also outline a realistic EU-anchored roadmap for translation that leverages existing infrastructures (EXPeRT, ERN PaedCan). We find promising—yet preliminary—signals for early non-remission/recurrence modeling in pediatric DTC and interpretable survival prediction in pediatric ACT. For PGL and GEP-NEN, evidence remains adult-led (biochemical ML screening scores; CT/PET radiomics for metastatic risk or peptide receptor radionuclide therapy response) and serves primarily as methodological scaffolding for pediatrics. Cross-cutting insights include the centrality of calibration and validation hierarchy and the current limits of explainability (radiomics texture semantics; saliency ≠ mechanism). Translation is constrained by small datasets, domain shift across age groups and sites, limited external validation, and evolving regulatory expectations. We close with pragmatic, clinically anchored steps—benchmarks, multi-site pediatric validation, genotype-aware evaluation, and equity monitoring—to accelerate safe, equitable adoption in pediatric endocrine oncology. Full article

In this study, we developed a novel water quality model that integrated hydrodynamic, solute transport, and geochemical reactions processes. This model was built upon the open-source ELCIRC hydrodynamic model, the TVD-format solute transport model, and the PhreeqcRM geochemical reaction engine. The accuracy of the model was rigorously validated using a 2D chain decay analytical solution, demonstrating its capability to accurately simulate water flow, solute transport, and chemical reactions. To evaluate the practical applicability of the model, case studies involving the 2012 Huaihe River benzene leakage accident and the acetic acid leakage accident in the Gulei sea area were simulated. Findings indicate that the model effectively captures the diffusion and attenuation dynamics of the benzene contamination plume. Furthermore, it accurately depicts the reaction–diffusion interaction with seawater following acetic acid release. Notably, the versatility and flexibility of the model were further demonstrated by its ability to simulate a wide range of pollutants and their associated biochemical processes. This addresses the limitations of existing water quality models and provides a powerful tool for environmental monitoring and assessment. The results of this study offer valuable insights for improving water quality management and emergency response strategies in the face of environmental pollution incidents. Full article

Fermentation is a complex biochemical process that transforms brewer’s wort into beer. Beer fermentation is driven by yeast and is influenced by process parameters such as the content of fermentable sugars in wort, temperature, and pH. Traditional methods of modelling this process rely heavily on empirically tuned kinetic models. However, these models tend to be recipe-specific and often require retuning when processes change. This paper proposes a data-driven approach using a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, to model beer fermentation dynamics. By training the LSTM model on real-world fermentation data (1305 fermentations across ales, IPAs, lagers, and mixed-culture beers), including variables such as apparent extract (derived from specific gravity), temperature, and pH, we demonstrate that this technique can accurately predict key fermentation trajectories and support process monitoring and optimisation. When evaluated on representative medoid fermentations as one-step-ahead roll-outs over 0–300 h, the model produces accurate predictions with low errors and minimal residuals. These results show that the LSTM-based model provides accurate and robust predictions across beer styles and operating conditions, offering a practical alternative to traditional mechanistic kinetic models. This work highlights the potential of LSTM networks to enhance our understanding, monitoring, and control of fermentation processes, providing a scalable and efficient tool for both research and industrial applications. The findings suggest that LSTM models can be effectively adapted to model other fermentation processes in beverage production, opening new possibilities for advancing food science and engineering. Full article

Diabetic wounds exhibit impaired immune function, delayed neutrophils recruitment, and heightened infection risk which compromises early infection control and delays healing. We have demonstrated that topical CCL3 treatment restores neutrophil influx, reduces bacterial infection by ~99%, and accelerates wound healing in diabetic mice. As per Food and Drug Administration (FDA) Guidelines for Investigational New Drug (IND), we conducted a 14-day acute toxicity study in diabetic mice following a single topical administration of CCL3 at effective low dose (1 µg) and high dose (10 µg) per wound. Mice were monitored for clinical signs, body weight, and food intake throughout the study period. On day 14, serum biochemistry (ALT, AST, BUN, creatinine, metabolic markers) and histopathology of major organs (liver, kidney, heart, lungs, spleen) were assessed. CCL3-treated diabetic mice exhibited no adverse clinical effects. Hematological and biochemical parameters remained within normal limits, and histopathological analyses revealed no additional organ injury in CCL3-treated groups compared to diabetic control mice. Intriguingly, CCL3-treated mice showed improved ALT levels and reduced hepatic pathology, suggesting hepatoprotective effects and reduced serum IgG, indicating reduced systemic inflammation. Overall, our study demonstrates that diabetic mice tolerate topical CCL3 at doses up to 10 times the effective therapeutic concentration without evidence of systemic organ toxicity. These findings provide strong preclinical support for the translational development of CCL3 as a novel therapy for diabetic wound care. Full article

Hereditary tyrosinemia type 1 (HT1) is a rare metabolic disorder caused by fumarylacetoacetate hydrolase deficiency, leading to the accumulation of toxic metabolites such as fumarylacetoacetate (FAA) and succinylacetone (SA). We report an 11-year-old boy with poorly controlled HT1 who presented with a severe neurovisceral crisis after suboptimal adherence to nitisinone (NTBC) therapy, characterized by abdominal pain, hypertension, paralytic ileus, seizures, and profound hyponatremia. Biochemical evaluation revealed markedly elevated urinary δ-aminolevulinic acid (ALA), consistent with a porphyria-like metabolic decompensation, together with inappropriately increased plasma copeptin in the setting of hypotonic hyponatremia and clinical euvolemia, fulfilling diagnostic criteria for the syndrome of inappropriate antidiuretic hormone secretion (SIADH). Optimization of NTBC therapy combined with tailored fluid management resulted in complete clinical and biochemical recovery. This case supports a pathophysiological link between acute disruption of the heme–porphyrin pathway and inappropriate antidiuretic hormone secretion. In HT1, this susceptibility may be further amplified by FAA- and SA-mediated oxidative stress, mitochondrial dysfunction, and heme depletion, with an additional contribution from SA-associated renal tubular impairment. Overall, our findings underscore SIADH as a potentially underrecognized cause of acute hyponatremia in HT1 and highlight the importance of strict NTBC adherence and early monitoring of urinary ALA during metabolic decompensation. Full article

Grain aging during storage leads to quality deterioration and significant economic losses. Traditional analytical approaches are often labor-intensive, slow, and inadequate for modern intelligent grain storage management. This review summarizes recent advances in the intelligent discrimination of grain aging using volatile organic compound (VOC) fingerprints combined with machine learning (ML) techniques. It first outlines the biochemical mechanisms underlying grain aging and identifies VOCs as early and sensitive biomarkers for timely determination. The review then examines VOC determination methodologies, with a focus on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), for constructing volatile fingerprinting profiles, and discusses related method standardization. A central theme is the application of ML algorithms, including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN)) for feature extraction and pattern recognition in high-dimensional datasets, enabling effective discrimination of aging stages, spoilage types, and grain varieties. Despite these advances, key challenges remain, such as limited model generalizability, the lack of large-scale multi-source databases, and insufficient validation under real storage conditions. Finally, future directions are proposed that emphasize methodological standardization, algorithmic innovation, and system-level integration to support intelligent, non-destructive, real-time grain quality monitoring. This emerging framework provides a promising powerful pathway for enhancing global food security. Full article

Mass spectrometry-based metabolomics is a valuable tool for advancing pediatric health research. Along with nuclear magnetic resonance, it enables detailed biochemical analysis from minimal sample volumes, a critical feature for pediatric diagnosis. Metabolomics supports early detection of inherited metabolic disorders, monitors metabolic changes during growth, and identifies disease markers for a range of conditions, including metabolic, neurodevelopmental, oncological, and infectious diseases. Integrating metabolomic data with genomic, proteomic (i.e., multi-omics approaches), and clinical information enables more precise and preventive care by enhancing risk assessment and informing targeted treatments. However, routine clinical use faces several challenges, including establishing age- and sex-specific reference ranges, standardizing sample collection and processing, ensuring consistency across platforms and laboratories, expanding reference databases, and improving data comparability. Ethical and regulatory issues, including informed consent, data privacy, and equitable access, also require careful consideration. Advances in high-resolution and single-cell metabolomics, artificial intelligence for data analysis, and cost-effective testing are expected to address these barriers and support broader clinical adoption. As standards and data-sharing initiatives grow, metabolomics will play an increasingly important role in pediatric diagnostics and personalized care, enabling earlier disease detection, improved treatment monitoring, and better long-term outcomes for children. Full article

Background/Objectives: Hypothyroidism is highly prevalent among HD patients, due to cumulative disturbances in thyroid hormone synthesis, metabolism, and clearance. Subclinical hypothyroidism—defined by elevated TSH with normal fT₄—is common in HD, along with a distinct entity, the low-T₃ syndrome. This study aims to examine the predictors of hypothyroidism in HD and its impact on cardiovascular morbidity and mortality. Methods: We conducted a retrospective cohort study including 282 hemodialysis (HD) patients, with evaluated thyroid function and monitored from January 2022 to June 2025. A total of 66 (23.4%) patients with hypothyroidism were identified, 15 (5.31%) of whom had autoimmune thyroiditis. Subclinical hypothyroidism was documented in 31.81% of the hypothyroid patients. Results: Hypothyroidism occurred predominantly in females (63.63% vs. 41.2%, p ≤ 0.001) and was associated with higher BMI (27.856 ± 6.216 vs. 25.759 ± 6.080, p = 0.017), hypoalbuminemia (3.534 ± 0.547 vs. 3.725 ± 0.471, p = 0.006), elevated LDL-cholesterol and triglyceride levels, as well as with amiodarone use. Hypothyroidism was further associated with atrial fibrillation (33.33 vs. 19.9%, p = 0.022), coronary artery revascularization procedures (18.18% vs. 9.72%, p = 0.047), neoplastic disease (25.75% vs. 12.03%, p = 0.008), and cancer-related mortality (10.6% vs. 1.85%, p = 0.001). Multivariable regression analysis revealed the following predictors of hypothyroidism: female sex (OR 3.848, 95%CI 1.704–8.693, p = 0.001), BMI (OR 1.072, 95%CI 1.007–1.146, p = 0.031), hypoalbuminemia (OR 0.412, 95%CI 0.177–0.962, p = 0.040), hypertriglyceridemia (OR 1.088, 95% CI 1.001–1.016, p = 0.022) and amiodarone use (OR 6.698, 95%CI 1.744–25.722, p = 0.006). Patients with autoimmune thyroiditis did not exhibit clinical or biochemical differences compared with other hypothyroid patients. Subclinical hypothyroidism was associated with longer HD duration (10.476 ± 7.910 vs. 6.567 ± 5.541, p = 0.003), dyslipidemia, hypertension, atrial fibrillation and amiodarone use. Cardiovascular conditions—particularly atrial fibrillation and ischemic coronary disease requiring revascularization—are more common in HD patients with clinical or subclinical hypothyroidism. However, in our cohort, the Kaplan–Meier survival curves at 12, 24, and 36 months for patients with both subclinical and clinical hypothyroidism do not show significant differences in cardiac or overall mortality. Conclusions: The increased incidence of hypothyroidism in HD patients, together with its impact on cardiovascular pathology, underscores the need for multidisciplinary management and supports annual routine assessment of thyroid hormones—particularly in overweight or dyslipidemic patients and in those receiving amiodarone. Full article

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications. Full article

Invasive alien plants (IAPs) disrupt biodiversity, ecosystem functions, rural livelihoods, and human health/well-being. Hence, the negative impact of Cenchrus setaceus (syn. Pennisetum setaceum) as an invasive weed poses many concerns in terms of environmental and socio-economic impact. The abundance in previous research on invasion ecology, weed biology, and the management of C. setaceus establishes the chance to carry out an in-depth evaluation of this invasive alien species for a cohesive understanding, closely linked to policy development. This systematic review aims to provide a comprehensive evaluation of previous research, identify knowledge gaps, and incorporate recent practical research findings on C. setaceus to elucidate management options. Standard methods were used to collect the literary evidence on multiple thematic aspects linked with its traits and management. Results revealed the substantial negative impacts of C. setaceus on ecosystems, ascribed to multiple physiological, biochemical, and ecological features. Further, a multitude of plant traits such as rapid seed distribution and efficient reproductive strategies imposed serious challenges in the control of C. setaceus. Deployment of integrated control methods for at least three years in depleting seed bank conjunction by planting native grass may help in its confinement. In conclusion, policy measures like strict biosecurity/legal regulations, explicit elucidation of weed biology, early detection and response, ecological modeling, and long-term monitoring with community participation can expand the horizon of C. setaceus control and help achieve its sustainable management. Full article

Polyvinyl alcohol (PVA) hydrogels modified through radical polymerization under UV irradiation and Ce⁴⁺ ion treatment were investigated as a potential platform for developing highly sensitive biosensors for rapid biochemical oxygen demand analysis in water. These modifications enhance PVA physicochemical properties, including mechanical strength, stability, and biocompatibility, making it promising for immobilizing microorganisms in bioanalytical systems. A dual-mediator biosensor system using ferrocene (FC) and neutral red (NR) was developed with yeast Blastobotrys adeninivorans immobilized in modified PVA. The FC+NR–B. adeninivorans–PVA–Ce⁴⁺ system exhibited high sensitivity (linear range of 0.1–3.81 mgO₂/dm³), selectivity, and operational stability (up to 37 days service life), outperforming existing analogs. Testing with wastewater confirmed strong correlation with standard BOD₅, highlighting the potential for monitoring water quality. The described radical modification method is a simple and effective approach for creating sensitive and stable biosensors. It opens up new possibilities for environmental monitoring technology. Full article

The use of proteolytic enzymes in association with entomopathogenic fungi offers a promising alternative for improving the biological control of insect pests. This study evaluated the compatibility between Beauveria bassiana and papain and the effectiveness of their combined application in controlling Tenebrio molitor. Conidial viability in the presence of papain was monitored for 48 h and showed a reduction in germination from 100% to approximately 70%, without detrimental effects on fungal performance. Papain activity remained stable up to 12 h, declining afterward, indicating biochemical compatibility. Bioassays revealed significant differences among treatments (p < 0.01). In larvae, mortality ranged from 5.18 ± 0.19% in the control to 49.62 ± 2.00% with papain, 62.24 ± 0.58% with conidia, and 89.71 ± 1.06% in the combined treatment; papain and conidia alone did not differ statistically. In pupae, mortality reached 2.20 ± 0.00% in the control, 47.38 ± 0.69% with papain, 63.69 ± 0.69% with conidia, and 85.91 ± 0.84% with the combination, with all treatments differing significantly. Fungal reisolation confirmed typical B. bassiana development. Overall, the results show that papain does not compromise fungal viability and that its combination with B. bassiana enhances entomopathogenic activity, supporting its potential for integrated pest management. Full article

Background/Objectives: Combat trauma involving large joints is associated with a high risk of thromboinflammatory complications. Early identification of laboratory markers for hypercoagulability is essential to optimise perioperative management. This study aimed to evaluate the dynamics of inflammation and haemostasis indicators in patients with combat-related joint trauma and to identify the most informative markers for preoperative risk assessment. Methods: A total of 29 patients with combat injuries to the hip, knee, elbow, or ankle joints were examined. Blood samples were taken 1–3 days prior to surgery and again on the first postoperative day. Parameters of coagulation (e.g., PT, INR, fibrinogen, D-dimer, soluble fibrin complexes, antithrombin III), fibrinolysis, and inflammation (e.g., CRP, haptoglobin, sialic acid, ESR, LSI, LII) were analysed and compared to those of 30 healthy controls. Statistical analysis included Student’s t-test and Pearson’s correlation. Results: At baseline, patients demonstrated significant increases in inflammatory markers (CRP 64.2 ± 7.3 mg/L, ↑738.9%; haptoglobin 3.25 ± 0.4 g/L, ↑164.3%; ESR 46.8 ± 5.2 mm/h, ↑313.8%) and procoagulant activity (D-dimer 1.42 ± 0.18 µg/mL, ↑136.6%; fibrinogen 6.12 ± 0.51 g/L, ↑102.4%; soluble fibrin complexes 38.7 ± 4.9 mg/L, ↑597.3%), together with a reduction in antithrombin III activity (63.5 ± 6.2%, ↓39.5%) and prolonged fibrinolysis time (increase by 197%). Postoperatively, these abnormalities intensified, indicating a sustained thromboinflammatory response. Strong correlations were found between inflammatory and haemostatic markers. Conclusions: Combat trauma of large joints is associated with preoperative thromboinflammatory dysregulation, which is exacerbated by surgery. Monitoring specific biochemical and haematological markers—such as CRP, fibrinogen, D-dimer, and soluble fibrin complexes—may support preoperative risk assessment and postoperative monitoring strategies for hypercoagulable states in this high-risk group. These findings lay the groundwork for future prospective studies aimed at developing stratified therapeutic protocols and predictive models for thromboinflammatory complications in orthopaedic trauma care. Full article