Search Results (153)

Lactate is a vital biomarker for disease diagnosis and healthcare management. With the development of wearable sensors, by analyzing biofluids, such as sweat, saliva, and tears, it is possible to implement the in situ detection of lactate, which could provide clinical-grade data for early disease detection and personalized healthcare. Among them, non-enzymatic lactate electrochemical sensors (NELESs) are on the rise due to their quick response, are easily miniaturized, and have the ability to overcome the intrinsic disadvantages of enzymatic sensors. Compared with enzyme-based lactate sensors, NELESs could simplify the electrode preparation process, reduce the cost, and improve the sensing stability and service life. In this review, we introduce the significance of the real-time monitoring of lactate and highlight recent advances in wearable electrochemical sensors toward continuous lactate analysis in biofluids. In particular, metal nanomaterials have great potential in constructing NELESs due to their unique physical and chemical properties, which can be divided into four categories: bimetallic nanomaterials, transition metal chalcogenides (TMC), metal oxides, and layered double hydroxides. We discuss recent advances of these non-enzymatic lactate oxidation materials in detail, and provide some insights for the further development of NELESs through a comprehensive analysis. Full article

Lactate is a key metabolite with applications ranging from monitoring training efficiency to early sepsis detection and monitoring the metabolic state of cell cultures. In this study, a paper-based lactate sensor utilizing a fluorescent readout was developed. Unlike common lactate dehydrogenase (LDH)-based methods, these sensors use a green fluorescent protein (GFP) or mApple-coupled lactate binding domain, which provides a fluorescent readout upon lactate binding. We demonstrate that immobilizing these proteins on paper does not affect their ability to bind lactate and produce a fluorescent readout, by monitoring lactate levels in the cell culture supernatant applying different cell culture conditions. Full article

Aminoacyl-tRNA synthetases (AARSs), traditionally recognized for their essential role in protein synthesis, are now emerging as critical players in cancer pathogenesis through translation-independent functions. Lactate-derived lactylation, a post-translational modification, plays an increasingly important role in tumorigenesis in the context of high levels of lactate in tumor cells due to the Warburg effect. Current research has highlighted AARS1/2 as lactate sensors and lactyltransferases that catalyze global lysine lactylation in cancer cells and promote cancer proliferation, providing a new perspective for cancer therapy. This review synthesizes the canonical and non-canonical functions of AARS1/2, with a particular focus on their lactylation-related mechanisms; details how lactylation acts as a mechanistic bridge linking AARS1/2 to diverse oncogenic signaling pathways, thereby promoting cancer hallmarks such as metabolic reprogramming, uncontrolled proliferation, immune escape, and therapy resistance; and proposes strategies to target AARS1/2 or modulate relative lactylation, offering a potential avenue to translate these insights into effective cancer therapies. Full article

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge. Full article

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for the precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions. Full article

In this study, we explore the neuroprotective and modulatory potential of graphenic materials (GMs) in terms of the maturation of dopaminergic neurons and their capacity to counteract the cellular stress induced by toxins such as MPP⁺ (1-methyl-4-phenylpyridinium) and Tunicamycin. We found that GMs promote significant morphological changes in neuronal cells after prolonged exposure, enhancing both differentiation and cellular adhesion. Through structural analysis, we unveiled a complex organization of GMs and a marked upregulation of tyrosine hydroxylase (TH), a key marker of mature dopaminergic neurons. Under oxidative stress induced by MPP⁺, GMs significantly reduced the release of lactate dehydrogenase (LDH), indicating protection against mitochondrial damage. Moreover, GMs substantially decreased the levels of α-synuclein (α-Syn), a protein closely associated with neurodegenerative disorders such as Parkinson’s disease. Notably, partially reduced graphene oxide (PRGO) and fully reduced graphene oxide (FRGO) films were particularly effective at reducing α-Syn-associated toxicity compared to positive controls. Under conditions of endoplasmic reticulum (ER) stress triggered by Tunicamycin, GMs—especially PRGO microflakes—modulated the unfolded protein response (UPR) pathway. This effect was evidenced by the increased expression of BIP/GRP78 and the decreased phosphorylation of stress sensors such as PERK and eIF2α; this suggests that a protective role is played against ER stress. Additionally, GMs enhanced the synthesis of Torsin 1A, a chaperone protein involved in correcting protein folding defects, with PRGO microflakes showing up to a fivefold increase relative to the controls. Through the cFos analysis, we further revealed a pre-adaptive cellular response in GM-treated cells exposed to MPP⁺, with PRGO microflakes inducing a significant twofold increase in cFos expression compared to the positive control, indicating partial protection against oxidative stress. In conclusion, these results underscore GMs’ capacity to modulate the critical cellular pathways involved in oxidative, mitochondrial, and ER stress responses, positioning them as promising candidates for future neuroprotective and therapeutic strategies. Full article

Blood lactate (BLa) concentration is a pivotal biomarker of exercise intensity and physiological stress, which provides insights into athletic performance and recovery. However, traditional lactate measurement requires invasive blood sampling, which presents significant limitations, including procedural discomfort, infection risks, and impracticality for continuous monitoring. Though non-invasive measurements of BLa concentration have emerged, most rely on a single physiological indicator like heart rate and sweat rate, and their accuracy and reliability remain limited. To address these limitations, this study proposes an innovative multi-sensor fusion framework for non-invasive estimation of BLa. By leveraging the inherent multisystem and multidimensional coordination of human physiology during exercise, the framework integrates a range of physiological signals (e.g., heart rate variability and respiratory entropy) and biomechanical signals (e.g., motion data). We proposed a stacking ensemble model that leverages the complementary strengths of these signals and achieved exceptional predictive performance with near-perfect correlation (R² = 0.9661) while maintaining high precision (MAE = 0.1816 mmol/L) and robustness (RMSE = 0.5891 mmol/L). Furthermore, the model’s exceptional capability extends to blood lactate threshold detection with 98.15% classification accuracy, which is a critical metric for training intensity optimization. This approach provides a robust, non-invasive solution for continuous exercise intensity monitoring, demonstrating significant potential for optimizing athletic performance through real-time physiological assessment and data-driven training modulation. Full article

The assessment of pig welfare and health at abattoirs is crucial for ensuring both animal well-being and food safety. Traditional assessment methods often rely on human observation, which is time-consuming, subjective, and difficult to scale in high-throughput facilities. This systematic review addresses a crucial gap by identifying and evaluating non-invasive human-free diagnostic methods applicable in commercial settings. Following PRISMA guidelines, a total of 102 articles met the inclusion criteria. Thirteen distinct methods were identified and classified into three categories: biological sample analysis (5 methods; n = 80 articles), imaging and computer vision systems (4 methods; n = 19), and physiological and other sensors (4 methods; n = 24). Some articles assessed more than one method and are therefore counted in multiple categories. While no method achieved both high implementation and practicality, blood analysis for glucose and lactate, convolutional neural networks for lesion detection, and automated camera-based systems emerged as the most promising for practical integration into the slaughter flowline. Most techniques still face challenges related to automation, operator independence, and standardisation. Overall, this review highlights the growing potential of non-invasive methods in pig welfare evaluation and underscores the need for continued development and validation to facilitate their adoption into routine abattoir practices. Full article

Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge–cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming. Full article

While the Ankom RF system facilitates efficient high-throughput in vitro fermentation studies, its high cost and limited flexibility constrain its broader applicability. To address these limitations, we developed and validated a low-cost, modular gas monitoring system (FerME), assembled from commercially available components. To evaluate its performance and reproducibility relative to the Ankom RF system (Ankom Technology, Macedon, NY, USA), in vitro rumen fermentation experiments were conducted under strictly controlled and identical conditions. Whole rumen contents were collected approximately 2 h post-feeding from individual mid- or late-lactation dairy cows and immediately transported to the laboratory. Each fermenter received 50 mL of processed rumen fluid, 100 mL of anaerobically prepared artificial saliva buffer, and 1.2 g of the donor cow’s diet. Bottles were sealed with the respective system’s pressure sensors, flushed with CO₂, and incubated in a 50 L water bath maintained at 39 °C. FerME (New Taipei City, Taiwan) and Ankom RF fermenters were placed side-by-side to ensure uniform thermal conditions. To assess the effect of filter bag use, an additional trial employed Ankom F57 filter bags (Ankom Technology, Macedon, NY, USA; 25 μm pore size). Trial 1 revealed no significant differences in cumulative gas production, volatile fatty acids (VFAs), NH₃-N, or pH between systems (p > 0.05). However, the use of filter bags reduced gas output and increased propionate concentrations (p < 0.05). Trial 2, which employed filter bags in both systems, confirmed comparable results, with the FerME system demonstrating improved precision (CV: 4.8% vs. 13.2%). Gas composition (CH₄ + CO₂: 76–82%) and fermentation parameters remained consistent across systems (p > 0.05). Importantly, with 12 pressure sensors, the total cost of FerME was about half that of the Ankom RF system. Collectively, these findings demonstrate that FerME is a reliable, low-cost alternative for real-time rumen fermentation monitoring and could be suitable for studies in animal nutrition, methane mitigation, and related applications. Full article

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

We are reporting the development of a high-performance, non-enzymatic electrochemical biosensor for selective lactate detection, integrating laser-induced graphene (LIG), gold nanoparticles (AuNPs), and a molecularly imprinted polymer (MIP) synthesized from poly(3,4-ethylenedioxythiophene) (PEDOT). The LIG electrode offers a highly porous, conductive scaffold, while electrodeposited AuNPs enhance catalytic activity and signal amplification. The PEDOT-based MIP layer, electropolymerized via cyclic voltammetry, imparts molecular specificity by creating lactate-specific binding sites. Cyclic voltammetry confirmed successful molecular imprinting and enhanced interfacial electron transfer. The resulting LIG/AuNPs/MIP biosensor demonstrated a wide linear detection range from 0.1 µM to 2500 µM, with a sensitivity of 22.42 µA/log(µM) and a low limit of detection (0.035 µM). The sensor showed excellent selectivity against common electroactive interferents such as glucose and uric acid, long-term stability, and accurate recovery in artificial saliva (>95.7%), indicating strong potential for practical application. This enzyme-free platform offers a robust and scalable strategy for continuous lactate monitoring, particularly suited for wearable devices in sports performance monitoring and critical care diagnostics. Full article

Background: Melanoma, one of the most aggressive forms of skin cancer, has seen significant therapeutic advances with immune checkpoint inhibitors (ICIs). However, many patients fail to respond or develop resistance, creating the need for adjunct strategies. Objective: The objective of this study is to critically evaluate how specific dietary patterns and nutrient-derived metabolites modulate melanoma metabolism and immunotherapy outcomes, emphasizing translational implications. Methods: We performed an integrative review of preclinical and clinical studies investigating dietary interventions in melanoma models and ICI-treated patients. Mechanistic insights were extracted from studies on nutrient transport, immunometabolism, and microbiome–immune interactions, including data from ongoing nutritional clinical trials. Results: Diets rich in fermentable fibers, plant polyphenols, and unsaturated lipids, such as Mediterranean and ketogenic diets, seem to contribute to the reprogramming of tumor metabolism and enhance CD8+ T-cell activity. Fasting-mimicking and methionine-restricted diets modulate T-cell fitness and tumor vulnerability via nutrient stress sensors (e.g., UPR, mTOR). High fiber intake correlates with favorable gut microbiota and improved ICI efficacy, while excess protein, methionine, or refined carbohydrates impair immune surveillance via lactate accumulation and immunosuppressive myeloid recruitment. Several dietary molecules act as network-level modulators of host and microbial proteins, with parallels to known drug scaffolds. Conclusions: Integrating dietary interventions into melanoma immunotherapy can significantly influence metabolic reprogramming by targeting metabolic vulnerabilities and reshaping the tumor–immune–microbiome axis. When combined with AI-driven nutrient–protein interaction mapping, this approach offers a precision nutrition paradigm that supports both physicians and patients, emerging as a novel layer to enhance and consolidate existing therapeutic strategies. Full article