A computational model of Alzheimer's disease at the nano, micro, and macroscales.

Introduction: Mathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive understanding of interactions among various components and facilitating in silico testing of intervention strategies. Alzheimer's disease (AD) is characterized by multifactorial causes and intricate interactions among biological entities, necessitating a personalized approach due to the lack of effective treatments. Therefore, mathematical models offer promise as indispensable tools in combating AD. However, existing models in this emerging field often suffer from limitations such as inadequate validation or a narrow focus on single proteins or pathways. Methods: In this paper, we present a multiscale mathematical model that describes the progression of AD through a system of 19 ordinary differential equations. The equations describe the evolution of proteins (nanoscale), cell populations (microscale), and organ-level structures (macroscale) over a 50-year lifespan, as they relate to amyloid and tau accumulation, inflammation, and neuronal death. Results: Distinguishing our model is a robust foundation in biological principles, ensuring improved justification for the included equations, and rigorous parameter justification derived from published experimental literature. Conclusion: This model represents an essential initial step toward constructing a predictive framework, which holds significant potential for identifying effective therapeutic targets in the fight against AD.

Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study.

BACKGROUND: Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN. METHOD: To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process. RESULTS: The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia. CONCLUSIONS: Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.

A simplified longitudinal model for the development of Type 2 Diabetes Mellitus.

Obesity and diabetes are a progressively more and more deleterious hallmark of modern, well fed societies. In order to study the potential impact of strategies designed to obviate the pathological consequences of detrimental lifestyles, a model for the development of Type 2 diabetes geared towards large population simulations would be useful. The present work introduces such a model, representing in simplified fashion the interplay between average glycemia, average insulinemia and functional beta-cell mass, and incorporating the effects of excess food intake or, conversely, of physical activity levels. Qualitative properties of the model are formally established and simulations are shown as examples of its use.

Current examining methods and mathematical models of horizontal transfer of antibiotic resistance genes in the environment.

The increasing prevalence of antibiotic resistance genes (ARGs) in the environment has garnered significant attention due to their health risk to human beings. Horizontal gene transfer (HGT) is considered as an important way for ARG dissemination. There are four general routes of HGT, including conjugation, transformation, transduction and vesiduction. Selection of appropriate examining methods is crucial for comprehensively understanding characteristics and mechanisms of different HGT ways. Moreover, combined with the results obtained from different experimental methods, mathematical models could be established and serve as a powerful tool for predicting ARG transfer dynamics and frequencies. However, current reviews of HGT for ARG spread mainly focus on its influencing factors and mechanisms, overlooking the important roles of examining methods and models. This review, therefore, delineated four pathways of HGT, summarized the strengths and limitations of current examining methods, and provided a comprehensive summing-up of mathematical models pertaining to three main HGT ways of conjugation, transformation and transduction. Finally, deficiencies in current studies were discussed, and proposed the future perspectives to better understand and assess the risks of ARG dissemination through HGT.

Monitoring progress towards the elimination of hepatitis C as a public health threat in Norway: a modelling study among people who inject drugs and immigrants.

BACKGROUND: The global incidence target for the elimination of hepatitis C among people who inject drugs (PWID) is <2/100. In Norway, the hepatitis C epidemic is concentrated in PWID. Immigrants are the second most important risk group for chronic infection. We modelled the incidence of hepatitis C among active PWID, and the prevalence of chronic infection among active PWID, ex-PWID and immigrants in Norway until 2022. METHODS: We built a stochastic compartmental model, which was informed using data from national data sources, literature, and expert opinion. We report median values with 95% credible intervals (CrI). RESULTS: The model estimated 30 (95% Crl: 13-52) new infections among active PWID in 2022, or 0.37/100 (95% Crl: 0.17-0.65), down from a peak of 726 (95% Crl: 506-1,067) in 2000. Across all groups, the model estimated 3,202 (95% Crl: 1,273-6,601) chronically infected persons in 2022. Results were robust in sensitivity analyses. CONCLUSIONS: Norway provides an example of the feasibility of hepatitis C elimination in a setting with a concentrated epidemic, high coverage of harm reduction services and no treatment restrictions. Continued momentum is needed to further reduce the transmission and burden of hepatitis C in Norway.

Four-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic components.

Introduction: Computational muscle force models aim to mathematically represent the mechanics of movement and the factors influencing force generation. These tools allow the prediction of the nonlinear and task-related muscle behavior, aiding biomechanics, sports science, and rehabilitation. Despite often overlooking muscle fatigue in low-force scenarios, these simulations are crucial for high-intensity activities where fatigue and force loss play a significant role. Applications include functional electrical stimulation, motor control, and ergonomic considerations in diverse contexts, encompassing rehabilitation and the prevention of injuries in sports and workplaces. Methods: In this work, the authors enhance the pre-existing 3CCr muscle fatigue model by introducing an additional component of force decay associated with central fatigue and a long-term fatigue state. The innovative four-compartment model distinguishes between the short-term fatigued state (related to metabolic inhibition) and the long-term fatigued state (emulating central fatigue and potential microtraumas). Results: Its validation process involved experimental measurements during both short- and long-duration exercises, shedding light on the limitations of the traditional 3CCr in addressing dynamic force profiles.

Predicting the long-term collective behaviour of fish pairs with deep learning.

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.

Mathematical models of coagulation-are we there yet?

BACKGROUND: Mathematical models of coagulation have been developed to mirror thrombin generation in plasma, with the aim of investigating how variation in coagulation factor levels regulates hemostasis. However, current models vary in the reactions they capture and the reaction rates used, and their validation is restricted by a lack of large coherent datasets, resulting in questioning of their utility. OBJECTIVES: To address this debate, we systematically assessed current models against a large dataset, using plasma coagulation factor levels from 348 individuals with normal hemostasis to identify the causes of these variations. METHODS: We compared model predictions with measured thrombin generation, quantifying and comparing the ability of each model to predict thrombin generation, the contributions of the individual reactions, and their dependence on reaction rates. RESULTS: We found that no current model predicted the hemostatic response across the whole cohort and all produced thrombin generation curves that did not resemble those obtained experimentally. Our analysis has identified the key reactions that lead to differential model predictions, where experimental uncertainty leads to variability in predictions, and we determined reactions that have a high influence on measured thrombin generation, such as the contribution of factor XI. CONCLUSION: This systematic assessment of models of coagulation, using large dataset inputs, points to ways in which these models can be improved. A model that accurately reflects the effects of the multiple subtle variations in an individual's hemostatic profile could be used for assessing antithrombotics or as a tool for precision medicine.

Network switches and their role in circadian clocks.

Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms.

Efficient Estimates of Surface Diffusion Parameters for Spatio-Temporally Resolved Virus Replication Dynamics.

Advanced methods of treatment are needed to fight the threats of virus-transmitted diseases and pandemics. Often, they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the hepatitis C virus (HCV) proceeds under the influence of nonstructural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5A protein. The diffusion of NSPs has been studied by in vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDEs). Previous work estimated the diffusion coefficient of the NS5A protein by minimizing the discrepancy between an extended set of sufPDE simulations and experimental FRAP time-series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region to its contour length. The quality of this estimate is explored by a comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Comparative anatomy of the Sapajus sp. (bearded capuchin) hand with comments on tool use in a parallel evolution with the hominid pathway.

Introduction: Bearded capuchins display a wide variety of manipulatory skills and make routine use of tools in both captivity and the wild. The efficient handling of objects in this genus has led several investigators to assume near-human thumb movements, despite a lack of anatomical studies. Methods: Here, we performed an anatomical analysis of muscles and bones in the capuchin hand. Sapajus morphological traits were quantitatively compared with those of humans, chimpanzees, gorillas, and baboons. Results: The comparative analysis indicated that the Sapajus hand is more similar to that of baboons and least similar to that of humans according to the muscles, bones, and three-dimensional data. Furthermore, these findings suggest that bearded capuchins lack true thumb opponency. Regarding manipulatory skills, they display rather primitive hand traits, with limited resources for precision grasping using the opponens pollicis. Discussion: These findings suggest that bearded capuchins' complex use of tools depends more heavily on their high cognitive abilities than on a versatile hand apparatus. These findings offer crucial insights into the evolution of primate cognition.

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model.

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Recreating metabolic interactions of the tumour microenvironment.

Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes.

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to (1) compare single-trait random regression models under a single-step genomic BLUP setting based on alternative covariance functions (i.e., Wood, Wilmink, and Ali and Schaeffer) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); (2) combine the best functions for each trait under a multiple-trait framework; (3) estimate time-dependent SNP effects for all the studied longitudinal traits; and (4) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The Wood model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across days in milk (DIM) for all traits ranged from -0.06 (186-215 DIM for MY-SCS) to 0.78 (66-95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Rho of Plants patterning: linking mathematical models and molecular diversity.

ROPs (Rho of Plants) are plant specific small GTPases involved in many membrane patterning processes and play important roles in the establishment and communication of cell polarity. These small GTPases can produce a wide variety of patterns, ranging from a single cluster in tip-growing root hairs and pollen tubes to an oriented stripe pattern controlling protoxylem cell wall deposition. For an understanding of what controls these various patterns, models are indispensable. Consequently, many modelling studies on small GTPase patterning exist, often focusing on yeast or animal cells. Multiple patterns occurring in plants, however, require the stable co-existence of multiple active ROP clusters, which does not occur with the most common yeast/animal models. The possibility of such patterns critically depends on the precise model formulation. Additionally, different small GTPases are usually treated interchangeably in models, even though plants possess two types of ROPs with distinct molecular properties, one of which is unique to plants. Furthermore, the shape and even the type of ROP patterns may be affected by the cortical cytoskeleton, and cortex composition and anisotropy differ dramatically between plants and animals. Here, we review insights into ROP patterning from modelling efforts across kingdoms, as well as some outstanding questions arising from these models and recent experimental findings.

Pharmacodynamic Age Structured Population Model For Cell Trafficking.

OBJECTIVES: Cell trafficking encompasses movement of the immune system cells (e.g., granulocytes, lymphocytes) between the blood and the extravascular tissues (e.g., lymph nodes). Corticosteroids are known to suppress cell trafficking. The age-structured cell population models introduce the transit time as a structure that allows one to quantify the distribution of times the immune cells spend in the blood and the extravascular tissues. The objective of this work is to develop an age-structured cell population model describing drug effects on cell trafficking and to implement the model in pharmacometric software to enable parameter estimation and simulations. METHODS: We adopted the well-known McKendrick age-structured population model to describe the age distributions in two cell populations: blood cells and cells in the extravascular space. The hazard of cell recirculation from the extravascular tissues was age dependent and described by the Weibull function with the shape ν and scale ß parameters. The drug effect on cell trafficking was modeled as the product of the Emax function of the drug plasma concentration and the Weibull hazard. The model was implemented in NONMEM 7.5.1. The model was applied to the basophil data in 34 healthy subjects who received a single intramuscular or oral dose of 6 mg dexamethasone (DEX). A recently published pharmacokinetic model was applied to describe DEX plasma concentration. Typical values of parameter estimates were further used to simulate the DEX effect of the basophil mean transit time in the extravascular tissues. RESULTS: Simulations of basophil time courses for varying ν demonstrated that the rebound in the blood count data following drug administration is only possible for ν >1. The estimates of model parameters were ν = 3.02, ß = 0.00863 1/h, and IC50 = 7.47 ng/mL. The calculated baseline mean transit times of basophils in the blood 7.2 h and extravascular tissues 104.9 h agree with the values reported in the literature. CONCLUSIONS: We introduced an age-structured population model to describe cell trafficking between the blood and extravascular tissues. The model was adopted to account for the inhibitory drug effect on the cell recirculation. We showed that the age structure is essential to explain the rebound observed in the blood count response to a single dose drug administration. The model was validated using the basophil responses to DEX treatment in healthy subjects.

The Impact of Rapid Drug Susceptibility Tests on Gonorrhea Burden and the Life Span of Antibiotic Treatments: A Modeling Study Among Men Who Have Sex With Men in the United States.

Rapid point-of-care tests that diagnose gonococcal infections and identify susceptibility to antibiotics enable individualized treatment. This could improve patient outcomes and slow the emergence and spread of antibiotic resistance. However, little is known about the long-term impact of such diagnostics on the burden of gonorrhea and the effective life span of antibiotics. We used a mathematical model of gonorrhea transmission among men who have sex with men in the United States to project the annual rate of reported gonorrhea cases and the effective life span of ceftriaxone, the recommended antibiotic for first-line treatment of gonorrhea, as well as 2 previously recommended antibiotics, ciprofloxacin and tetracycline, when a rapid drug susceptibility test that estimates susceptibility to ciprofloxacin and tetracycline is available. The use of a rapid drug susceptibility test with ≥50% sensitivity and ≥95% specificity, defined in terms of correct ascertainment of drug susceptibility and nonsusceptibility status, could increase the combined effective life span of ciprofloxacin, tetracycline, and ceftriaxone by at least 2 years over 25 years of simulation. If test specificity is imperfect, however, the increase in the effective life span of antibiotics is accompanied by an increase in the rate of reported gonorrhea cases even under perfect sensitivity.

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models.

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Understanding patient needs and predicting outcomes in IgA nephropathy using data analytics and artificial intelligence: a narrative review.

This narrative review explores two case scenarios related to immunoglobulin A nephropathy (IgAN) and the application of predictive monitoring, big data analysis and artificial intelligence (AI) in improving treatment outcomes. The first scenario discusses how online service providers accurately understand consumer preferences and needs through the use of AI-powered big data analysis. The author, a clinical nephrologist, contemplates the potential application of similar methodologies, including AI, in his medical practice to better understand and meet patient needs. The second scenario presents a case study of a 20-year-old man with IgAN. The patient exhibited recurring symptoms, including gross haematuria and tonsillitis, over a 2-year period. Through histological examination and treatment with renin-angiotensin system blockade and corticosteroids, the patient experienced significant improvement in kidney function and reduced proteinuria over 15 years of follow-up. The case highlights the importance of individualized treatment strategies and the use of predictive tools, such as AI-based predictive models, in assessing treatment response and predicting long-term outcomes in IgAN patients. The article further discusses the collection and analysis of real-world big data, including electronic health records, for studying disease natural history, predicting treatment responses and identifying prognostic biomarkers. Challenges in integrating data from various sources and issues such as missing data and data processing limitations are also addressed. Mathematical models, including logistic regression and Cox regression analysis, are discussed for predicting clinical outcomes and analysing changes in variables over time. Additionally, the application of machine learning algorithms, including AI techniques, in analysing big data and predicting outcomes in IgAN is explored. In conclusion, the article highlights the potential benefits of leveraging AI-powered big data analysis, predictive monitoring and machine learning algorithms to enhance patient care and improve treatment outcomes in IgAN.

Prediction models of carcass characteristics from non­castrated Nellore cattle finished in the feedlot system under tropical conditions.

Our objective was to use measures of intake and productive performance to adjust prediction models for the carcass traits of non-castrated Nellore cattle finished in a feedlot. Individual data from 168 non-castrated male Nellore steers finished in feedlot between the years 2016-2021 were used. Descriptive statistical analyzes and Pearson correlation coefficients were performed. The outliers were tested by evaluating the studentized residuals in relation to the values predicted by the equations. Residues that were outside the range of -2.5 to 2.5 were removed. The goodness of fit of the developed equations was evaluated by the coefficients of determination (R2) and root mean square error (RMSE). Models for carcass yield, subcutaneous fat thickness, ribeye area, and shear force were adjusted. Means of 53.5% carcass yield, 4.8 mm subcutaneous fat thickness, 73 cm2 loin eye area, and 8.1 kg shear force were observed. The observed average intakes were 9.9 kg/day of dry matter, 3.3 kg/day of neutral detergent fiber content, 1.5 kg/day of crude protein, and 7.1 kg/day of total digestible nutrients. The average confinement time was 113 days, the average total weight gain was 152.2 kg and the average daily gain was 1.35 kg/day. Intake measures significantly correlated with shear force and subcutaneous fat thickness and ribeye area. Carcass yield was significantly correlated with total weight gain, feedlot time, and hot carcass weight. Measures of nutrient intake, performance, and confinement time can be used as predictors of carcass yield, ribeye area, fat thickness, and shear force of non-castrated Nellore cattle finished in a feedlot. The prediction equations for ribeye area, carcass yield, subcutaneous fat thickness, and shear force showed sufficient precision and accuracy for non-castrated Nellore cattle finished in confinement systems under tropical conditions. All equations can be used with caution to estimate carcass traits of cattle finished in a feedlot using measures of intake and productive performance.