Search Results (36)

The gut microbiota of macaques, highly homologous to humans in biological characteristics and metabolic functions, serves as an ideal model for studying the mechanisms of human intestinal diseases and therapeutic approaches. A comprehensive characterization of the macaque gut microbiota provides unique insights into human health and disease. This study employs metagenomic sequencing to assess the gut microbiota of wild M. mulatta brevicaudus across various ages, sexes, and physiological states. The results revealed that the dominant bacterial species in various age groups included Segatella copri and Bifidobacterium adolescentis. The predominant bacterial species in various sexes included Alistipes senegalensis and Parabacteroides (specifically Parabacteroides merdae, Parabacteroides johnsonii, and Parabacteroides sp. CT06). The dominant species during lactation and non-lactation periods were identified as Alistipes indistinctus and Capnocytophaga haemolytica. Functional analysis revealed significant enrichment in pathways such as global and overview maps, carbohydrate metabolism and amino acid metabolism. This study enhances our understanding of how age, sex, and physiological states shape the gut microbiota in M. mulatta brevicaudus, offering a foundation for future research on (1) host–microbiome interactions in primate evolution, and (2) translational applications in human health, such as microbiome-based therapies for metabolic or immune-related disorders. Full article

Eventing is an Olympic equestrian discipline comprising dressage, cross-country, and show jumping, with the cross-country phase imposing the greatest physical demands on horses. This study presents a composite model to estimate energy expenditure during the cross-country phase, integrating physiological data (heart rate-derived $V{O}_2$ and lactate-based anaerobic estimates) with external workload indicators (GPS-derived speed, elevation, and course complexity). Model development was based on 691 rides from 256 horses across 232 events at 2-star to 5-star competition levels. The analysis showed that terrain, speed variability, and acceleration, largely shaped by course design, significantly affect energy expenditure. Aerobic and anaerobic contributions to power output varied by speed, format, and competition level. The model explained 29% of variance in power output and 91% when accounting for random effects, demonstrating the influence of both external and individual factors. Short-format events exhibited higher anaerobic contributions than long-format events. While the competition level had a modest effect, it reflected increasing technical difficulty and jump size. These findings underline the importance of incorporating both physiological responses and course characteristics in energy assessments. The model supports more targeted conditioning, enhances performance monitoring, and contributes to improved equine welfare by providing a more accurate understanding of workload in cross-country competitions. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

Background: Global health organizations recommend breastfeeding, but maternal pharmacotherapy can disrupt this due to safety concerns. Physiologically based pharmacokinetic (PBPK) models predict medication transfer through breastfeeding, relying on validated milk intake volume data. However, the literature is mainly focused on different measurement methods, or such intake data have been collected without systematic review. This systematic review therefore aims to gather data on human milk intake volume derived using the (dose-to-the-mother) deuterium oxide dilution method, allowing for comparison with the literature. Additionally, it aims to explore the effects of maternal conditions on milk intake volume. Methods: PubMed, Embase, Web of science, Cochrane library, Scopus and CINAHL were searched for studies on the dilution method and breastfeeding in healthy infants. Risk of bias was assessed using the Newcastle–Ottawa scale (NOS) and the Risk of Bias 2 (RoB2) tool. Data on mean human milk intake volume were extracted and synthesized (mL/day and mL/kg/day) throughout infancy. Results: Sixty studies (34 countries) reported on the milk intake volume of 5502 infants. This intake was best described by logarithmic regression y(mL/kg/day) = 149.4002 − 0.2268 × x − 0.1365 × log(x) (x = postnatal age, days). Maternal conditions showed no significant influence on human milk intake, except for maternal smoking (reduction). Conclusions: This function corresponds with previous research, particularly for infants aged between 1.5 and 12 months. The limited availability of early infancy data underscores the need for additional data for future PBPK modeling to enhance informed healthcare decisions and improve outcomes for mothers and infants. Full article

Objective: This study aimed to investigate the relationship between the intake of solid block dairy products like cheese and serum uric acid levels, along with its potential physiological mechanisms. Methods: Data for our study were obtained from the Chinese Children and Lactating Women Nutrition and Health Surveillance. Generalized linear models and restricted cubic splines were employed to analyze the relationship between the intake of solid block dairy products like cheese and serum uric acid levels. Two-sample Mendelian randomization (TSMR) analysis was conducted to infer causality, based on a large sample size and robust methodology. Gene Ontology (GO) enrichment analysis was also performed to identify potential biological pathways. Results: Among all types of dairy products, a significant negative association with serum uric acid levels was observed only for the intake of solid block dairy products like cheese, regardless of covariate adjustment (β = −0.182, p < 0.001). TSMR results supported a negative causal relationship between cheese intake and serum uric acid levels (β = −0.103, 95% CI: −0.149 to −0.057; p = 0.002). The JAK-STAT signaling pathway and autophagy regulation were identified as potential physiological mechanisms underlying this relationship. Conclusions: The intake of solid block dairy products like cheese was found to result in decreased levels of serum uric acid, with potential mechanisms involving the JAK-STAT signaling pathway and the regulation of autophagy. Full article

In recent years, 3D cell culture systems have emerged as sophisticated in vitro models, providing valuable insights into human physiology and diseases. The transition from traditional 2D to advanced 3D cultures has introduced novel obstacles, complicating the characterization and analysis of these models. While the lactate dehydrogenase (LDH) activity assay has long been a standard readout for viability and cytotoxicity assessments in 2D cultures, its applicability in long-term 3D cultures is hindered by inappropriate normalization and low LDH stability over time. In response to these challenges, we propose an optimization of LDH assays, including a crucial normalization step based on total protein quantification and a storage method using an LDH preservation buffer. We applied it to compare unexposed cerebral organoids with organoids exposed to a toxic dose of valproic acid, and showed efficient normalization of cellular viability as well as enhanced LDH stability within the buffer. Importantly, normalized LDH activity results obtained were independent of organoid dimension and cell density. This refined LDH assay, tailored to address 3D culture constraints, allows for the transposition of this routine test from 2D to 3D cultures. Full article

There is growing concern about the potential adverse health effects of phthalates (PAEs) on human health and the environment due to their extensive use as plasticizers and additives in commercial and consumer products. In this study, we assessed PAE concentrations in serum samples from aquarium-based delphinids (Tursiops truncatus, n = 36; Orcinus orca, n = 42) from California, Florida, and Texas, USA. To better understand the physiological effects of phthalates on delphinids, we also explored potential correlations between phthalates and the biomarkers aldosterone, cortisol, corticosterone, hydrogen peroxide, and malondialdehyde while accounting for sex, age, and reproductive stage. All PAEs were detected in at least one of the individuals. ΣPAE ranges were 5.995–2743 ng·mL⁻¹ in bottlenose dolphins and 5.372–88,675 ng·mL⁻¹ in killer whales. Both species displayed higher mean concentrations of DEP and DEHP. PAEs were detected in newborn delphinids, indicating transference via placenta and/or lactation. Linear mixed model results indicated significant correlations between aldosterone, month, location, status, and ΣPAEs in killer whales, suggesting that aldosterone concentrations are likely affected by the cumulative effects of these variables. This study expands on the knowledge of delphinid physiological responses to PAEs and may influence management and conservation decisions on contamination discharge regulations near these species. Full article

(1) Background: There is a lack of data on the long-term training characteristics and performance markers of elite young female endurance athletes. The aim of this study was to present the training load (ECOs), as well as the evolution of the anthropometric values and performance of three elite U23 female triathletes over a season. (2) Methods: General training data and performance data relating to the swimming, cycling, and running legs of the 2021 season were described. The training intensity distribution (TID) was presented using the triphasic model, while the training load was based on the ECO model. An anthropometric analysis was also conducted in accordance with the ISAK standards. (3) Results: Triathletes increased their VO_2max in cycling (6.9–10%) and running (7.1–9.1%), as well as their power and speed associated with the VO_2max (7.7–8.6% in cycling and 5.1–5.3% in running) and their swimming speed associated with the lactate thresholds (2.6–4.0% in LT2 and 1.2–2.5% in LT1). The triathletes completed more than 10 h of weekly average training time, with peak weeks exceeding 15 h. The average TID of the three triathletes was 82% in phase 1, 6% in phase 2, and 12% in phase 3. A decrease in the sum of skinfolds and fat mass percentage was observed during the season in the three triathletes, although the last measurement revealed a stagnation or slight rise in these parameters. (4) Conclusions: The triathletes performed a combination of two training periodization models (traditional and block periodization) with a polarized TID in most of the weeks of the season. Improvements in performance and physiological parameters were observed after the general preparatory period as well as a positive body composition evolution throughout the season, except at the end, where the last measurement revealed stagnation or a slight decline. This study can be useful as a general guide for endurance coaches to organize a training season with female U23 triathletes. Full article

Currently, research predominantly focuses on evaluating clinical effects at specific time points while neglecting underlying patterns within the treatment process. This study aims to analyze the dynamic alterations in PANSS total scores and prolactin levels in patients with schizophrenia treated with risperidone, along with the influencing covariates. Using data from an 8-week randomized, double-blind, multicenter clinical trial, a population pharmacodynamic model was established for the PANSS total scores of and prolactin levels in patients treated with risperidone. The base model employed was the E_max model. Covariate selection was conducted using a stepwise forward inclusion and backward elimination approach. A total of 144 patients were included in this analysis, with 807 PANSS total scores and 531 prolactin concentration values. The PANSS total scores of the patients treated with risperidone decreased over time, fitting a proportionally parameterized sigmoid E_max model with covariates including baseline score, course of the disease, gender, plasma calcium ions, and lactate dehydrogenase levels. The increase in prolactin levels conformed to the ordinary E_max model, with covariates encompassing course of the disease, gender, weight, red blood cell count, and triglyceride levels. The impacts of the baseline scores and the course of the disease on the reduction of the PANSS scores, as well as the influence of gender on the elevation of prolactin levels, each exceeded 20%. This study provides valuable quantitative data regarding PANSS total scores and prolactin levels among patients undergoing risperidone treatment across various physiological conditions. Full article

Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2–4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models. Full article

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (C_max) and area under the concentration–time curve (AUC_{(0–24 h)}) for breastmilk were higher than in plasma (C_max: 155 vs. 69.9 ng/mL; AUC_{(0–24 h)}: 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC_{(0–24 h)} ratio increased up to three-fold (3.4–3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations. Full article

The concept of the quick sequential organ failure assessment (qSOFA) simplifies sepsis detection, and the next SOFA should be analyzed subsequently to diagnose sepsis. However, it does not include the concept of suspected infection. Thus, we simply developed a biomarker-based assessment model for detecting sepsis (BADS). We retrospectively reviewed the electronic health records of patients admitted to the intensive care unit (ICU) of a 2000-bed university tertiary referral hospital in South Korea. A total of 989 patients were enrolled, with 77.4% (n = 765) of them having sepsis. The patients were divided into a ratio of 8:2 and assigned to a training and a validation set. We used logistic regression analysis and the Hosmer–Lemeshow test to derive the BADS and assess the model. BADS was developed by analyzing the variables and then assigning weights to the selected variables: mean arterial pressure, shock index, lactate, and procalcitonin. The area under the curve was 0.754, 0.615, 0.763, and 0.668 for BADS, qSOFA, SOFA, and acute physiology and chronic health evaluation (APACHE) II, respectively, showing that BADS is not inferior in sepsis prediction compared with SOFA. BADS could be a simple scoring method to detect sepsis in critically ill patients quickly at the bedside. Full article

The estimation of anthocyanin (Anth) content is very important for observing the physiological state of plants under environmental stress. The objective of this study was to estimate the Anth of maize leaves at different growth stages based on remote sensing methods. In this study, the hyperspectral reflectance and the corresponding Anth of maize leaves were measured at the critical growth stages of nodulation, tasseling, lactation, and finishing of maize. First-order differential spectra (FD) were derived from the original spectra (OS). First, the spectral parameters highly correlated with Anth were selected. A total of two sensitive bands (R_λ), five classical vegetation indices (VI_S), and six optimized vegetation indices (VI_C) were selected from the original and first-order spectra. Then, univariate regression models for Anth estimation (Anth-UR models) and multivariate regression models for estimating anthocyanins (Anth-MR models) were constructed based on these parameters at different growth stages of maize. It was shown that the first-order spectral conversion could effectively improve the correlation between R_λ, VI_C, and Anth, and VI_C are usually more sensitive to Anth than VI_S. In addition, the overall performance of Anth-MR models was better than that of Anth-UR models. Among them, Anth-MR models with the combination of three types of spectral parameters (FD(R_λ) + OS_VI_C + FD_VI_C/VI_S) as inputs had the best overall performance. Moreover, different growth stages had an impact on the Anth estimation models, with tasseling and lactation stages showing better results. The best-performing Anth-MR models for these two growth stages were as follows. For the tasseling stage, the best model was the FD(R_λ) + OS_VI_C + VI_S-based SVM model, with an R² of 0.868, RMSE of 0.007, and RPD of 2.19. For the lactation stage, the best-performing model was the FD(R_λ) + OS_VI_C + FD_VI_C-based RF model, with an R² of 0.797, RMSE of 0.007, and RPD of 2.24. These results will provide a scientific basis for better monitoring of Anth using remote sensing hyperspectral techniques. Full article

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for “non-lactating” adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (C_max) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage. Full article