Search Results (258)

This comment discusses a recent review by Wu and colleagues on multimodal artificial intelligence in gastroenterology and hepatology. The review outlined advancements in endoscopic, radiomics, pathologic, and multi-omics technologies. Additionally, it highlights persistent barriers, such as data heterogeneity, “black box” opacity, reimbursement uncertainty, and third-party data security risks. The comment also discusses current payment models for autonomous algorithms and emphasizes the importance of robust governance frameworks. Beyond summarizing recent progress, this commentary proposes a pragmatic, five-point roadmap to facilitate the safe and fair deployment of multimodal artificial intelligence in digestive disease care in the future, including standardization, explainability, federated governance, equitable reimbursement, and sustainability metrics. By implementing these action items, stakeholders can transform promising algorithms into clinically validated, workflow-compatible, and economically viable tools. Full article

This study examined the digestive and metabolic responses of Naozhou (NZ) stock large yellow croaker (Larimichthys crocea) larvae and juveniles under five developmental feeding stages (DAH3, DAH7, DAH12, DAH19, DAH49) to clarify mechanisms of early nutritional adaptation. Digestive enzyme assays, transcriptome sequencing, and metabolomics were integrated to compare physiological changes across diets. Protease activity increased sharply from DAH7–19 with the introduction of rotifers, Artemia, and copepods, while amylase and lipase activities rose at DAH19–49, reflecting enhanced carbohydrate and lipid utilization during transition to formulated feeds. Transcriptomic analysis showed that differentially expressed genes were enriched in pathways involving protein digestion, lipid and energy metabolism, and cell cycle regulation. The metabolomic analysis further highlighted dynamic changes in amino acid, lipid, carbohydrate, and vitamin metabolism, consistent with transcriptomic findings. The integrated analysis suggests that the coordinated modulation of digestive enzyme activities, gene expression, and metabolite profiles enabled a smooth transition from yolk dependency to live prey feeding and a subsequent use of artificial diets. These findings provide new insights into the early nutritional development of NZ large yellow croaker and provide a scientific basis for the improvement of artificial aquaculture seed production. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components of Brachiaria hybrid cv. Ipyporã, using three datasets composed of pasture management variables, chemical composition variables, and their combination. Artificial neural network (Multilayer Perceptron, MLP), decision trees (REPTree and M5P), Random Forest (RF), and Multiple Linear Regression (LR) were tested. The principal component analysis revealed that 61.3% of the total variance was explained by two components, highlighting a strong association between digestibility and crude protein content and an opposite relationship with lignin and neutral detergent fiber. Among the evaluated models, MLP, LR, and RF achieved the best performance for leaf digestibility (r = 0.76), while for stem digestibility the highest accuracy was obtained with the LR model (r = 0.79; MAE = 2.42; RMAE = 2.87). The REPTree algorithm presented the lowest predictive performance regardless of the input data. The results indicate that chemical composition variables alone are sufficient to develop reliable prediction models. These findings demonstrate the potential of ML techniques as a non-destructive and cost-effective approach to predict the nutritional quality of tropical forage grasses and support precision livestock management. Full article

In the face of increasingly severe water environmental pollution and energy shortages, anaerobic digestion (AD) technology has demonstrated immense potential for the resource recovery of wastewaters such as food waste leachate (FWL). However, the inherent drawback of the long experimental period required for AD severely constrains research efficiency. Existing studies often rely on either kinetic models with high interpretability or machine learning models with strong generalization capabilities, rarely integrating both. To address this, this study innovatively investigated the anaerobic co-digestion of enzyme-modified biodegradable plastics (BPs) and FWL, and constructed a novel Physics-Informed Neural Network (PINN) based on a dataset of 261 experimental observations. The results indicated that, among the three kinetic models, the Modified Gompertz model exhibited the best prediction accuracy (R² approaching 0.99), stability, and universality. Among the four machine learning models, the Artificial Neural Network (ANN) demonstrated optimal generalization ability (Test set R² = 0.958). Notably, the constructed Modified Gompertz PINN model achieved superior predictive performance (Test set R² = 0.994), reducing the Root Mean Square Error (RMSE) by 74.0% compared to the ANN model. Shapley analysis further confirmed the PINN retained strong biological rationality, indicating that the hydrolysis process significantly impacts methane production. This work provides a robust hybrid model for efficient co-digestion prediction and offers a new approach for the resource valorization of enzyme-modified BPs and FWL. Full article

Artificial intelligence (AI) has emerged as an innovative approach to the computer modeling and optimization of anaerobic digestion (AD) and anaerobic co-digestion (AcoD) processes. AI-based algorithms are ideally suited to capture the complex nonlinear behavior of these processes. Compared to conventional methods and models, AI-based algorithms have made modeling these processes much easier. Various AI algorithms, including multivariate statistical analyses, tree-based machine learning, nature-inspired optimization, support vector machine, and artificial neural networks (ANN) have been widely used to model the AD and AcoD processes. Researchers have successfully used stand-alone and hybrid ANMs to predict biogas yield and composition, as well as for efficient process monitoring and control. Furthermore, the development of advanced optimization algorithms, including genetic algorithms and particle swarm optimization, helps to optimize the ratio of mixing of co-substrates in AcoD and important process parameters (i.e., temperature (T), pH, retention time, total solids and volatile solids). This review discusses AI applications for AD and AcoD process modeling, optimization, prediction of unknown parameters and variables, and real-time monitoring and control. A critical comparison is made with some of the popular mathematical models and algorithms for monitoring and optimization designed on their basis. The review presents also future research directions in this area and some of our own results. Full article

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article

Plant-based proteins offer sustainable alternatives to animal sources, yet their lower digestibility remains a critical barrier to widespread applications. Current digestibility assessment methods require days of analysis and gram-scale samples, creating significant bottlenecks in protein optimization workflows. This study developed an ensembled deep learning framework that transforms digestibility prediction from a resource-intensive process to a rapid, minimal-sample assessment. By systematically characterizing 23 diverse plant protein isolates across multiple physicochemical dimensions, we trained a feedforward neural network based on augmented data. Our model identified α-helix content, random coil content, and solubility as key digestibility indicators. This insight enabled the construction of a streamlined three-feature model that reduced assessment time by 80% while requiring only one-hundredth of standard sample amounts. When validated against independent published datasets, the model achieved rational prediction accuracy, with an R² = 0.91. These findings establish a transformative framework for accelerating plant protein development, enabling rapid screening of novel sources and targeted modification strategies to enhance nutritional bioavailability, ultimately advancing sustainable food system transitions. Full article

The health and productivity of dairy cows are critical factors in sustainable livestock management. Along with the rapid rise in intelligence and technology, applying intelligence in livestock management helps in monitoring and provide precise and effective care for the cattle herd. This research designs an intelligent system that can assist the farmers and predict Gir cows’ diseases and a support system powered by Artificial Intelligence (AI). The proposed system integrates Internet of Things (IoT) and sensors to track and monitor critical health parameters of the Gir cow, which includes the step count, lying time, rumination time, heart rate, and various environmental factors contributing to the well-being of the cow. The data points that are gathered from the sensors is then processed and analysed using machine learning (ML) algorithms, including Random Forest (RF), decision tree (DT), Logistic Regression, K-Neighbours, and Support Vector Machine (SVM) to predict abnormalities including diseases such as lameness, mastitis, heat stress, and digestive problems. The AI techniques used in the system involve complex data processing and pattern recognition to identify early signs of diseases. The RF and DT ML models achieved the highest accuracy (100%), while SVM demonstrated robust performance with 94% accuracy. Integrating real-time monitoring with predictive analytics enables early detection of health issues, allowing timely interventions and improving overall herd management. The proposed system enhances cow welfare and optimises farm productivity but also has the potential to revolutionise the dairy industry. The complex intelligent system provides a reliable and efficient platform for disease prediction and herd management, and can significantly contribute to the sustainability and profitability of dairy farming, thereby shaping the future of the industry. Full article

The intestine is a complex organ whose main functions are food digestion and nutrient absorption. It is therefore of great interest for pharmaceutical research as a preferred route for drug delivery. In vitro intestinal models are valuable tools for the preclinical evaluation of absorption, distribution, metabolism, and excretion of new therapeutic formulations; consequently, several attempts have been made to recreate the human intestine barrier in vitro. The models so far set up were aimed at mimicking specific intestinal features related to the molecules or processes under investigation. Artificial membranes are suitable to study passive absorption; systems based on 2D/3D cell cultures reproduce the transcellular pathway; organs-on-a-chip mimic the in vivo cellular and mechanical complexity, allowing the identification of the multiple factors involved in molecular interactions with the intestinal barrier; and intestine explants replicate in full the native organ under controlled conditions, thus providing the most comprehensive in vitro model. All these models have advantages and disadvantages but all have given important contribution to advance the knowledge on the interaction of drugs, toxins, and xenobiotic with the intestinal barrier. Full article

Insects present extraordinary potential for obtaining recombinant proteins, both in terms of the quantity and quality of the synthesized product. This work proposes the use of artificial diets including pDNA as an oral transfection system for the Lepidoptera Spodoptera exigua. It is hypothesized that oral transfection can lead to the effective expression of the reporter genes carried in plasmids. Prior to their incorporation into the artificial diet, plasmids (pCMVβ and pEGFP-N2) were protected from inactivation in the digestive tract by chitosan nanoparticulation. The survival of plasmids and their oral uptake by larvae was evaluated, as well as the persistence of pDNA in larvae throughout their ontogeny. The results confirmed that transfection occurred and that pDNA persisted during the ontogeny, even after discontinuing plasmid administration. The transcription of reporter genes was quantified by qRT-PCR, and the results indicate a dose-dependent synthesis of mRNA as the inclusion level of pDNA in diets increased. Moreover, the measurement of the biological activity of the recombinant proteins (β-galactosidase activity and green fluorescence) paralleled the results obtained for gene transcription, also dose-dependently. Therefore, effective oral transfection is feasible in S. exigua, provided that pDNA is protected against gut inactivation prior to its incorporation in artificial diets. Full article

Precision Feeding Systems (PFS) demonstrate transformative potential in advancing sustainable and efficient production within modern animal husbandry. However, existing research lacks a synthesis of PFS applications in livestock farming and offers little targeted guidance for China’s rapidly growing rabbit industry. The objective of this review is to bridge this gap by synthesizing current knowledge on PFS technologies—including sensor networks, artificial intelligence (AI), automated controls, and data analytics—and providing a structured framework for their implementation in rabbit production. This study selects and analyzes 112 core references, establishing a foundational database for comprehensive evaluation. The key contributions of this work are threefold: first, it outlines the core components and operational mechanisms of PFS; second, it identifies major challenges such as sensor reliability in dynamic environments, data security risks, limited explainability of AI models, and interoperability barriers; and third, it proposes a customized strategy for PFS adoption in rabbit farming, emphasizing phased implementation, cross-system integration, and iterative optimization. The primary outcomes and advantages of adopting such a system include significant improvements in feed efficiency, resource utilization, animal welfare, and waste reduction—critical factors given rabbits’ sensitive digestive systems and precise nutritional needs. Furthermore, this review outlines a future research agenda aimed at developing resilient sensors, explainable AI frameworks, and multi-objective optimization engines to enhance the commercial scalability and sustainability of PFS in rabbit husbandry and beyond. Full article

Peptide mapping is a well-established method for confirming the identity of therapeutic proteins as part of batch release testing and product characterization for regulatory filings. Traditionally based on enzymatic digestion followed by reversed-phase liquid chromatography and UV detection, the method has evolved with technological advancements to incorporate mass spectrometry (MS), enabling more detailed structural insights. Residue-level confirmation of amino acid sequences requires MS/MS fragmentation, which produces large amounts of data that must be processed using specialized software. In regulated environments, the use of academic algorithms is often limited by validation requirements, making it necessary to rely on commercially approved tools, although their built-in scoring systems have limitations that can affect sequence assignment accuracy. Here, we present representative examples of incorrect peptide assignments generated by commercial software. In antibody sequence analysis, misidentifications resulted from isobaric and near-isobaric dipeptides (e.g., SA vs. GT). Additional examples from the analysis of SARS-CoV-2 spike protein variants revealed software-induced artifacts, including artificial succinylation of aspartic acid residues to compensate for sequence mismatches, and incorrect deamidation site assignments due to misinterpretation of isotopic peaks. These findings underscore the necessity for expert manual review of MS/MS data, even when using validated commercial platforms, and highlight the molecular challenges in distinguishing true sequence variants from software-driven artifacts. Full article

To understand the changes in gonadal sex differentiation, digestive enzyme activity, and growth-related hormone levels in the larval and juvenile black scraper, Thamnaconus modestus, continuous sampling was conducted from 0 to 91 days post-hatching (dph). 17β-estradiol (E2) and testosterone (T) levels, six digestive enzymes, as well as T3, T4, GH, and IGF-I were detected. The results showed that oogonia or spermatogonia was observed at 60 dph. During the sex differentiation to female or male, both E2 and T levels significantly increased (p < 0.05), suggesting that E2 and T may induce the sex differentiation to female or male in T. modestus, respectively. The amylase activity from 0 to 35 dph showed a slow upward trend, which may be due to the transition from endogenous to exogenous nutrition at this time. From 12 to 25 dph, alkaline protease activity significantly decreased (p < 0.05), while acid protease levels significantly increased (p < 0.05), suggesting that as organs in the digestive system continue to develop, acid protease plays an important role. T3 and T4 could already be detected at 0 dph, and the T4 content was always much higher than T3 throughout the stages, indicating that T4 may play more important roles than T3. Additionally, the changes in IGF-I and GH content followed a trend of an initial increase, a subsequent decrease, and then an increase, ultimately showing an overall upward trend. These results indicate that T4, IGF-I, and GH play crucial roles in growth and development in the juvenile fish. In conclusion, the results of this study provide useful information for growth, artificial reproduction, and sex regulation in T. modestus. Full article

At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex “black boxes,” with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians’ trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human–machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice. Full article