Search Results (3,085)

High non-esterified fatty acids (NEFAs) during negative energy balance in dairy cattle can impair reproduction. While their effects on oocyte maturation and preimplantation embryo development are known, their impact during fertilisation is largely unexplored. This study examined the effects of high NEFA exposure exclusively during in vitro fertilisation (IVF). Bovine oocytes were matured in vitro and fertilised under physiological or high NEFA concentrations. High NEFA concentrations decreased fertilisation, cleavage, and blastocyst rates. Reactive oxygen species production in zygotes was not affected, but blastocysts derived from the High-NEFA group had fewer cells. Spermatozoa exposed to high NEFA concentrations exhibited increased plasma membrane and acrosome damage, higher DNA fragmentation, and reduced mitochondrial membrane potential. The expression of H3K27me3, a repressive histone mark normally erased from fertilisation to embryonic genome activation, was higher in 2-cell than in 4-cell embryos on day 2 after IVF, but only in the High-NEFA group. This delayed H3K27me3 loss, along with increased DNA damage, could partially explain the reduced blastocyst formation observed. In conclusion, high NEFA concentrations can impair pre-implantation embryo development during zygote formation, potentially via effects on both the oocyte and spermatozoon. The latter warrants further investigation using an intracytoplasmic sperm injection model. Full article

Background: In late January 2025, a measles outbreak began in Gaines County, Texas, USA, and the outbreak extended to New Mexico. We used a variety of mathematical models to estimate the growth rate of the Texas–New Mexico measles outbreak of 2025. Methods: We used both empirical and mechanistic models based on differential equations to make the estimations that allow us to characterize this measles outbreak. Regarding empirical models, we used the exponential growth model to compute and estimate the growth rate, basic reproduction number, ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. With regard to mechanistic models, we use the SIR and SEIR models to estimate the growth rate, basic reproduction number ${\mathcal{R}}_0$, and effective reproduction number ${\mathcal{R}}_t$. We used new weekly measles cases and also cumulative cases. Results: Using the exponential growth model, we estimated a basic reproduction number between 32 and 40. For the classical SIR model, we estimated that the basic reproduction number is approximately 30. Conclusion: We found that the current Texas–New Mexico measles outbreak of 2025 has a slightly higher growth rate and effective reproduction number ${\mathcal{R}}_t$ compared to several previous measles outbreaks around the world. Full article

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Allorecognition, or distinguishing between the self and nonself within the same species, is observed in both animals and plants, particularly in the context of immune reactions and self-incompatibility in sexual reproduction. Polymorphic recognition molecules are known to be responsible for such allorecognition during fertilization. Previous studies have reported that in ascidians and flowering plants, inbreeding avoidance relies on a pair of polymorphic recognition molecules with a receptor-ligand relationship that are encoded at a single locus, the S locus (Self-incompatibility locus), but the process by which such pairs of recognition molecules emerge and evolve to become polymorphic is not known. Here, a population genetics study was carried out as a novel approach for investigating allorecognition. To study the process by which self-recognition emerges, we simulated a situation in which an allorecognizing genotype is generated from a nonallorecognizing genotype through mutation and then analyzed whether the two genotypes could coexist. The conditions under which the numbers of allorecognition alleles could increase over evolutionary time were investigated, and the generational dynamics of nonallorecognizing genotypes were analyzed. Subsequent modeling was carried out to reproduce the allorecognition mechanism in Ciona, and consistency between the simulation results and experimental data was observed. Our approach provides new insight into the evolutionary process of allorecognition. Full article

Endometrial carcinoma (EC) is the most common malignancy of the female reproductive tract, with increasing incidence driven by aging populations and obesity. While molecular classification has improved diagnostic precision, the identification of clinically relevant metabolic biomarkers remains incomplete, and targeted therapies are not yet standardized. In this study, we investigated metabolic alterations in four EC cell lines (AN3-CA, EFE-184, HEC-1B and MFE-296) compared to non-malignant controls under normoxic and stress conditions (hypoxia and lactic acidosis) to identify metabolomic differences with potential clinical relevance. Untargeted gas chromatography–mass spectrometry (GC/MS) and targeted liquid chromatography–mass spectrometry (LC/MS) profiling revealed two distinct metabolic subtypes of EC. Cells of metabolic subtype 1 (AN3-CA and EFE-184) exhibited high biosynthetic and energy demands, enhanced cholesterol and hexosyl-ceramides synthesis and increased RNA stability, consistent with classical cancer-associated metabolic reprogramming. Cells of metabolic subtype 2 (HEC-1B and MFE-296) displayed a phospholipid-dominant metabolic profile and greater hypoxia tolerance, suggesting enhanced tumor aggressiveness and metastatic potential. Key metabolic findings were validated via real-time quantitative PCR. This study identifies and characterizes distinct metabolic subtypes of EC within the investigated cancer cell lines, thereby contributing to a better understanding of tumor heterogeneity. The results provide a basis for potential diagnostic differentiation based on specific metabolic profiles and may support the identification of novel therapeutic targets. Further validation in three-dimensional culture models and ultimately patient-derived samples is required to assess clinical relevance and integration with current molecular classifications. Full article

Background: Infertility remains a significant global health challenge, affecting approximately 15% of couples worldwide. In vitro fertilization (IVF) has transformed reproductive medicine; however, challenges such as low success rates in older patients, ovarian insufficiency, endometrial dysfunction, and male infertility continue to limit outcomes. Objective: This review aims to summarize the principles of IVF and explore the potential role of stem cells in enhancing IVF outcomes, with particular attention to applications in both women and men, as well as the accompanying ethical considerations. Summary: Stem cell research has introduced novel therapeutic opportunities, including ovarian rejuvenation, endometrial regeneration, sperm quality enhancement, and the development of synthetic embryo models. Mesenchymal stem cells (MSCs), embryonic stem cells (ESCs), and induced pluripotent stem cells (iPSCs) demonstrate regenerative properties that may help to overcome current reproductive limitations. Despite encouraging findings from preclinical and early clinical studies, challenges such as tumorigenesis, genetic instability, and ethical controversies remain major barriers to translation. Conclusions: IVF continues to serve as a cornerstone of assisted reproductive technology (ART). Stem cell-based approaches represent an exciting frontier that could expand the therapeutic possibilities of IVF. Careful clinical validation, international regulatory harmonization, and robust ethical oversight will be essential to ensuring safe and equitable implementation. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Managing invasive species such as the Chinese sleeper (Perccottus glenii) supports the goals of sustainable development by preserving native biodiversity. This study investigated the population structure, growth, and ecological impact of P. glenii in a small, anthropogenically altered tributary of the Vistula River (central Poland). Electrofishing surveys conducted between 2017 and 2023 assessed sex ratio, age structure, body size, condition (Fulton’s index), and growth parameters, as well as changes in the local fish community. The sex ratio was nearly balanced (♀:♂ = 1.00:0.99), and average standard length and weight were 6.54 cm/9.11 g (females) and 6.36 cm/7.69 g (males). Dominant individuals were from age group of 2+ years. The Fulton condition factor ranged from 2.54 to 2.58, while positive algometric growth was observed for both sexes. The von Bertalanffy growth model parameters (L∞ = 175.37 mm, k = 0.104, t₀ = −1.711) revealed slower growth compared to other Eurasian populations. In the individual months of the study, changes in the sex structure, length, weight, and age of the fish were observed. This seasonality may have resulted from physiological changes (including fish growth and reproductive processes), their migration, and environmental changes, such as food availability and hydrochemical parameters, occurring during this period. Additionally, over the study period, the abundance and density of P. glenii increased significantly, coinciding with a marked decline in native fish species. These findings highlight the adaptability of this invasive fish and emphasise the need for targeted management strategies in degraded freshwater ecosystems. Full article

The female reproductive system represents a highly complex regulatory network governing critical physiological functions, encompassing reproductive capacity and endocrine regulation that maintains female physiological homeostasis. The in vitro simulation system provides a novel tool for biomedical research and can be used as physiological and pathological models to study the female reproductive system. Recent advances in this technology have evolved from 2D and 3D printing to organ-on-a-chip (OOC) and microfluidic systems, which has emerged as a transformative platform for modeling the female reproductive system. These microphysiological systems integrate microfluidics, 3D cell culture, and biomimetic scaffolds to replicate key functional aspects of reproductive organs and tissues. They have enabled precise simulation of hormonal regulation, embryo-endometrium interactions, and disease mechanisms such as endometriosis and gynecologic cancers. This review highlights the current state of female reproductive OOCs, including ovary-, uterus-, and fallopian tube-on-a-chip system, their applications in assisted reproduction and disease modeling, and the technological hurdles to their widespread application. Though significant barriers remain in scaling OOCs for high-throughput drug screening, standardizing protocols for clinical applications, and validating their predictive value against human patient outcomes, OOCs have emerged as a transformative platform to model complex pathologies, offering unprecedented insights into disease mechanisms and personalized therapeutic interventions. Future directions, including multi-organ integration for systemic reproductive modeling, incorporation of microbiome interactions, and clinical translation for mechanisms of drug action, will facilitate unprecedented insights into reproductive physiology and pathology. Full article

Microplastics (MPs) are plastic particles smaller than 5 mm that accumulate in ecosystems and can cause toxicity in organisms by affecting multiple biological processes. This study investigates the effects of poly(methyl methacrylate) microplastic microspheres (MPs, 200 µm diameter) on Caenorhabditis elegans, a widely used model in ecotoxicology. Nematodes were exposed to MPs at concentrations of 0.01, 0.1, 1, and 10 mg/mL, and various toxicological endpoints were assessed. The uptake of MPs was evaluated by µFT-IR analysis. The results indicate that MPs induce a concentration-dependent reduction in body length and alterations in the reproduction rate. Lifespan was also significantly reduced, with a 20% decrease at the highest concentration. Intestinal permeability assays revealed disruption of gut integrity at higher concentrations, and oxidative stress analysis showed a 1.8-fold increase in reactive oxygen species (ROS) levels at 10 mg/mL. Gene expression analysis via real-time qPCR indicated the upregulation of genes involved in oxidative stress and in DNA repair mechanisms. Additionally, the longevity-related transcription factors daf-16 and skn-1 were modulated, suggesting an adaptive stress response. These findings suggest that MPs impair growth, reproduction, and oxidative stress response in C. elegans, emphasizing the potential risks associated with microplastic exposure. Full article

The swimming crab is a commercially and nutritionally important marine resource with the highest catch volumes in Mexico occurring along the East Pacific coast. Among the Pacific species of the genus Callinectes, the blue crab C. arcuatus has the widest distribution and is found throughout the year. Its close resemblance to the well-studied Atlantic blue swimming crab (C. sapidus) makes it an excellent model for molecular reproductive studies in the Mexican Pacific. Using next-generation sequencing, this study aimed to characterize maturation-associated genes in an ovary–hepatopancreas transcriptome of C. arcuatus, with a particular focus on vitellogenin (Vtg) and its expression in the ovaries and hepatopancreas across different gonadal maturity stages. The transcriptome library generated from pooled samples produced 27,729 unigenes, of which, 196 (1.81%) were identified as reproduction-related genes. Notably, 33 of these genes, including the complete Vtg sequence, have not been previously reported in this species. Vtg expression was found to be tissue-specific, with levels in the hepatopancreas up to 13 orders of magnitude higher than in the ovary. In the hepatopancreas, Vtg expression increased exponentially from stage I to stage V of gonadal maturity, whereas in the ovaries, its expression showed the opposite trend. These findings highlight that the hepatopancreas, with its abundant nutrient reserves, serves as the primary site of Vtg expression and synthesis. Full article

According to the World Health Organization (WHO), globally, cervical cancer ranks as the fourth most common cancer in women, with around 660,000 new cases in 2022. In the same year, about 94 percent of the 350,000 deaths caused by cervical cancer occurred in low- and middle-income countries. This paper focuses on the dynamics of HPV by modeling the interactions between four compartments, as follows: S(t), the number of susceptible females; I(t), females infected with HPV; X(t), females infected with HPV but not yet affected by cervical cancer (CCE); and V(t), females infected with HPV and affected by CCE. A compartmental model is formulated to analyze the progression of HPV, ensuring all key mathematical properties, such as existence, uniqueness, positivity, and boundedness of the solution. The equilibria of the model, such as the HPV-free equilibrium and HPV-present equilibrium, are analyzed, and the basic reproduction number, $R_0$, is computed using the next-generation matrix method. Local and global stability of these equilibria are rigorously established to understand the conditions for disease eradication or persistence. Sensitivity analysis around the reproduction number is carried out using partial derivatives to identify critical parameters influencing $R_0$, which gives insights into effective intervention strategies. With appropriate positivity, boundedness, and numerical stability, a new stochastic non-standard finite difference (NSFD) scheme is developed for the proposed model. A comparison analysis of solutions shows that the NSFD scheme is the most consistent and reliable method for a stochastic fractional delay model. Graphical simulations are presented to provide visual insights into the development of the disease and lend the results to a more mature discourse. This research is crucial in highlighting the mathematical rigor and practical applicability of the proposed model, contributing to the understanding and control of HPV progression. Full article

Tetranychus mexicanus (McGregor, 1950) (Tetranychidae) is considered one of the primary phytosanitary problems in passion fruit crops, resulting in significant production losses. Understanding the impact of this mite species’ activity on the physiology of passion fruit plants can serve as a basis for developing sustainable management strategies. With this in mind, this research sought to analyze, using supervised and unsupervised machine learning models, how T. mexicanus mite infestation influences gas exchange, chlorophyll “a” and chlorophyll “b” levels, fluorescence, and thermal response of passion fruit plants. We tested the hypothesis that juvenile and adult mites alter the physiological and thermal response patterns of plants. Only the variables related to the fluorescent response (Fo, Fm, and Fv) had a significant relationship with mite infestation. In the joint comparison of multiple fluorescent variables, there were differences between the treatments of plants infested and not infested by T. mexicanus. The variables’ initial fluorescence (Fo), maximum fluorescence (Fm), and variable fluorescence (Fv) of chlorophyll a had a direct negative impact on both reproductive activity, as measured by the number of eggs and nymphs produced, and the total number of mites found. The unsupervised model based on multidimensional scaling with the k-means algorithm revealed a clear separation between the groups of infested passion fruit plants (Group 1) and healthy plants (Group 2). The Fo response was described with high accuracy for the reproductive rate (75%) and total infestation of eggs, nymphs, and adults of the mites (99.99%). Kappa values were moderate (Kappa = 0.50) and high (Kappa = 0.99) for reproductive and total rates of T. mexicanus, respectively. Additionally, the thermal response revealed that the infested passion fruit plants had a median temperature of 25.1 °C, compared to a median temperature of 25.7 °C, with notable differences between these medians. Therefore, the T. mexicanus mite altered both the fluorescent and thermal patterns of passion fruit plants. Our findings have implications for the development of early detection tools and the generation of future resistance breeding. Full article

Background: The ubiquitin–proteasome system plays a critical role in plant antiviral defense, with HECT-type E3 ubiquitin ligases serving as key regulators of protein turnover. To explore the potential involvement of the HECT gene family in host resistance against tomato yellow leaf curl virus (TYLCV), a comprehensive analysis was conducted in Nicotiana benthamiana. Methods: In this study, the HECT gene family in N. benthamiana was systematically investigated using a genome-wide bioinformatic analysis. The potential roles of these genes in the response to TYLCV infection were further examined using a virus-induced gene silencing (VIGS) technique. Results: Using a Hidden Markov Model approach, 18 NbHECT genes were identified that phylogenetically clustered into four subfamilies with distinct structural features. Chromosomal location and synteny analyses indicated that these genes were unevenly distributed across 11 chromosomes, with 10 instances of segmental duplication identified. Tissue-specific expression profiling demonstrated that 17 NbHECTs were constitutively expressed, with Group III members showing the highest expression in reproductive tissues. Following TYLCV infection, NbHECT6 was significantly downregulated while NbHECT13 was upregulated in both inoculated and systemic leaves. Functional validation through the VIGS approach revealed that suppression of NbHECT6 and NbHECT13 increased host susceptibility, as evidenced by exacerbated symptom severity and enhanced viral DNA accumulation compared to controls. Conclusions: These findings establish NbHECT6 and NbHECT13 as critical components of the plant antiviral response, providing new insights into ubiquitin-mediated defense mechanisms against geminiviruses. Full article

Endocrine axes are pathways of interactions involved in various aspects of the organism’s functioning, also implicated in deviations from physiological states leading to pathological conditions. The hypothalamic–pituitary–adrenal (HPA) axis releases corticosteroid hormones promoting adaptation to environmental stimuli (acute stress) or inducing altered conditions due to long-term noxious solicitations (chronic stress). The HP–gonadal (HPG) axis regulates reproductive activities by releasing gonadal steroids. These axes have been shown to engage in reciprocal inhibition under certain conditions, particularly when they rise beyond normal ultradian and circadian fluctuations. Based on the literature data, we reconstructed a neuroendocrine network responsible for this type of interaction. Thereafter, we developed a model of the HPA-HPG inhibition based on a series of nonlinear interactions represented by a system of differential equations in the Matlab environment. The quantitative analysis of the system’s behavior revealed the occurrence of bifurcations leading to bistable behavior, allowing us to detect bifurcation parameters. Bifurcation arises as the system’s components increase hypersensitivity and sustained activity in response to activating inputs. This involves transition from a single low-activity attractor to two distinct attractors, with a new high-activity state representing a breakdown of homeostasis. These results provide insights into the potential involvement of the HPA-HPG interaction in neuroendocrine disorders, and the identification of therapeutic targets from bifurcation parameters. Full article