Search Results (346)

The literature on pork quality perception is fragmented, particularly regarding the role of sensory and intrinsic attributes and consumer familiarity. This study addresses this gap by examining the importance of sensory attributes (juiciness, flavor, aroma, and tenderness) and an intrinsic attribute (pig breed—related to differences in color and fat content) in the overall quality assessment of pork loin. Additionally, it investigates how consumer familiarity with the pork loin category influences perceived quality. An experimental study was conducted with 130 Spanish consumers. The proposed hypotheses were tested using analysis of covariance (ANCOVA) models and a latent class cluster analysis to explore both the impact of specific attributes on perceived quality and the segmentation of consumers based on familiarity. The findings indicate that flavor, tenderness, and juiciness are the key sensory attributes influencing the overall quality perception of pork loin. In contrast, Duroc pork loin is perceived as being of lower quality. The cluster analysis identified three distinct consumer segments based on their level of familiarity with the product. This study contributes new empirical evidence to the understanding of the perceived quality of pork loin, highlighting the significant role of specific sensory attributes and consumer familiarity. These insights can inform product development and marketing strategies tailored to different consumer profiles. Full article

Nero Siciliano (NS) is an autochthonous pig breed reared in northeastern Sicily; despite its high-quality meat products, NS is currently endangered. This study aimed to evaluate the genetic variability at nine loci within candidate genes for meat traits—Melanocortin 4 Receptor (MC4R), Ryanodine Receptor 1 (RYR1), Class 3 Phosphoinositide 3-Kinase (PIK3C3) and Leptin (LEP)—to provide useful information for preservation and exploitation of the NS pig breed. Distribution of the genetic variants was assessed in a representative sample of 87 pigs (18 boars and 69 sows) collected in nine farms located in the original breeding area. Genotypes have been determined using PCR-RFLP and Sanger sequencing. Alleles linked to different growth rates and back fat deposition showed high frequencies (MC4R c.175C—0.93; LEP g.3469T—0.91) in the whole sample. Deviations from Hardy–Weinberg equilibrium and different allele distribution in boars and sows were observed. The RYR1 g.1843T allele, associated with Malignant Hyperthermia and Pale Soft Exudative meat defect, was reported in seven heterozygote pigs (q = 0.04) with one farm exhibiting a frequency of 0.29. Our results suggest the need for continuous monitoring of the genetic variants in NS both to maintain high meat quality and eradicate the RYR1 g.1843T allele. Full article

Background: Sport plays an important role in the health promotion of people with cerebral palsy (CP). However, risk factors may impair sport performance and health in non-ambulatory athletes. Therefore, the aim of the present study was to explore body composition and bone health in a group of world-class Boccia players with CP. Methods: Five BC2-class players with CP, aged 15–42 years old, were assessed using Dual-Energy X-Ray Absorptiometry (DXA) for body composition and bone mineral density (BMD) and content (BMC). The fat mass index (kg/m²) was used to define obesity, and the BMD Z-score used to analyze bone health. A preliminary indicator of sarcopenia was considered using the appendicular lean mass index. Results: Players 1 and 3 exhibited similar body compositions (obesity class 1 and BMD Z-score are below the expected range for age). Player 5 exhibited multiple health-related risk factors. The results regarding youth players (Player 2 and Player 4) should be analyzed with caution. Conclusions: Overall, due to Boccia’s specific characteristics, players may benefit from close monitoring by multidisciplinary teams and supplementary strategies (e.g., strength training, individualized diet plans) to promote quality of life and performance. However, further research is needed to confirm the data, since these preliminary findings do not allow for broader generalizations. Full article

Artemisia absinthium L. contributes to ecological stabilization in arid regions through its deep root system for sand fixation and soil microenvironment modulation, thereby effectively mitigating desertification. Total terpenoids have been extracted from A. absinthium (AATP) and found to have antioxidant and anti-inflammatory activities. Terpenoids are a class of natural products derived from methyl hydroxypropanoic acid, for which their structural units consist of multiple isoprene (C5) units. They are one of the largest and most structurally diverse classes of natural compounds. However, there are still large gaps in knowledge regarding their exact biological activities and effects. Atherosclerosis (AS) is a prevalent cardiovascular disease marked by the chronic inflammation of the vascular system, and lipid metabolism plays a key role in its pathogenesis. This study determined the extraction and purification processes of AATP through single-factor experiments and response surface optimization methods. The purity of AATP was increased from 20.85% ± 0.94 before purification to 52.21% ± 0.75, which is 2.5 times higher than before purification. Studies have shown that the total terpenoids of A. absinthium significantly reduced four indices of serum lipids in atherosclerosis (AS) rats, thereby promoting lipid metabolism, inhibiting inflammatory processes, and hindering aortic wall thickening and hepatic fat accumulation. It is known from network pharmacology studies that AATP regulates the Janus kinase/signal transducer (JAK/STAT) signaling axis. Molecular docking studies have indicated that the active component of AATP effectively binds to Janus kinase (JAK2) and signal transducer (STAT3) target proteins. The results indicate that AATP can inhibit the release of pro-inflammatory mediators (such as reactive oxygen species (ROS)) in LPS-induced RAW264.7 macrophages. It also inhibits the M1 polarization of RAW264.7 macrophages. Protein immunoblotting analysis revealed that it significantly reduces the phosphorylation levels of Janus kinase (JAK2) and the signal transducer and activator of transcription 3 (STAT3). Research indicates that the active components in A. absinthium may exert anti-atherosclerotic effects by regulating lipid metabolism and inhibiting inflammatory responses. It holds potential value for development as a functional food or drug for the prevention and treatment of atherosclerosis. Full article

The association between nutrition and endometriosis is controversial. This umbrella review aimed to investigate whether specific dietetic strategies are useful for reducing endometriosis risk/symptoms. Systematic reviews on diet therapies for endometriosis were analyzed using the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis methodology, and an umbrella review was implemented using Jamovi software. The 10 included systematic reviews comprised observational studies (cohort, case–control, cross-sectional) and interventional trials (randomized, non-randomized). A mild (class IV, lowest strength on evidence quartile) protective effect on vegetables (RR 0.590; 95% CI 0.49–0.71 p < 0.001), cheese (OR 0.840; 95% CI 0.74–0.96 p = 0.011), total dairy (RR 0.874; 95% CI 0.81–0.95 p = 0.001), and high-fat dairy (RR 0.590; 95% CI 0.81–0.99 p = 0.025) was found. Butter (RR 1.266; 95% CI 1.03–1.55 p = 0.024) and high caffeine (>300 mg/day) (RR 1.303; 95% CI 1.05–1.62 p = 0.019) consumption increased the risk of endometriosis. Other food groups had low-quality evidence due to limited studies. A higher intake of vegetables and dairy products may reduce the risk and/or symptoms of endometriosis, while a high intake of caffeine and butter may increase the risk. However, the heterogeneity across studies is significant, and the overall quality of the findings is low. Therefore, it is crucial to conduct new research in this field, focusing on well-designed randomized trials. Full article

This study was conducted to characterize and classify soils and rocks and to produce an engineering geological map that is beneficial for overall urban planning. The soils’ moisture content and specific gravity values range from 23.47% to 44.21% and 2.68 to 2.81, respectively. The activity of soils varies from 0.34 to 0.78 (inactive to normal). The shrinkage limit and shrinkage index values of soils range from 5% to 11.43% and 14.29% to 26.9%, respectively. Free swell value varies from 5 to 23% (low expansive). The unconfined compressive strength of soils ranges from 215.8 to 333.5 kPa (very stiff). According to USCS (Unified Soil Classification System), soils are classified into lean clay, lean clay with sand, fat clay with sand, and clayey silt with slight plasticity. According to BSCS (British Soil Classification SystemS), soils are classified into clay of intermediate plasticity, clay of high plasticity, and silt of intermediate plasticity. Rocks were classified into four categories based on their mass strength: very low mass strength, low mass strength, medium mass strength, and high mass strength. The RQD Rock Quality Designatione) value ranges from 47.48% to 98.25%, indicating a quality range from poor to excellent. The RMR Rock Mass Ratinge) values range from 44 to 90%, indicating that the rocks of the study area fall into three major classes: Class I (very good), Class II (good), and Class III (fair). 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

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida’s SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes. Full article

Background/Objectives: Cardiometabolic disease (CD) risk factors refer to the conditions that increase the likelihood of developing several health complications. The purpose of this study was to identify latent classes of CD risk factors among Brazilian adolescents and their association with sociodemographic and lifestyle behaviors. Methods: This was a cross-sectional study involving 349 adolescents aged 15 to 19 years old. A latent class analysis (LCA) was performed based on body mass index, body fat percentage, waist circumference, waist-to-height ratio, and blood pressure. Demographic characteristics and lifestyle variables related to screen time (ST), moderate-to-vigorous physical activity (MVPA), sedentary behavior (SB), and sleep duration were assessed through questionnaires. Results: Three CD risk factor classes were identified as follows: “Low Risk” (Class 1 = 79.5% of the sample), “Moderate Risk” (Class 2 = 8.6%), and “High Risk” (Class 3 = 11.9%). Sex and high ST (defined as >4 h/day) were associated with a greater likelihood of belonging to the higher CD risk classes. Adolescents with high ST presented a 4.39 (CI 95% 1.64–11.07) times greater chance of belonging to the “High Risk” instead of the “Low Risk” class. Adolescents with longer MVPA time had a higher probability of belonging to the “Low CD Risk” class. Conclusions: Female adolescents with less MVPA, more ST, and higher SB had a higher probability of being classified as “Higher CD Risk”. Efficient strategies to increase MVPA and reduce ST may contribute to the reduction in body fat accumulation and BP, which are the manifest variables in the proposed model. Full article

In the efforts towards germplasm innovation of livestock and poultry, strategies to improve meat quality have faced some increasingly challenging and dynamic concerns. Intramuscular fat (IMF) content and backfat thickness are two important traits contributing to meat quality. MicroRNAs (miRNAs)—a class of endogenous noncoding RNAs maintaining cell homeostasis by inhibiting target gene expression—have been proven as critical regulators of body fat deposition, thus affecting farm animal production. Our previous in vitro and in vivo models of pigs have clarified that miR-130b overexpression can obviously suppress adipogenesis of subcutaneous preadipocytes and lower backfat thickness. However, the way miR-130b regulates proliferation and adipogenesis of primary cultured porcine intramuscular preadipocytes (PIMPA) and the underlying mechanism are still unknown. PIMPA derived from longissimus dorsi muscle were employed to examine the role of miR-130b in proliferation and adipogenesis and to further elucidate its underlying mechanism. Lipid deposition in cytoplasm was evaluated by TG quantification and ORO-staining, and EDU-staining was employed to measure cell proliferation. Adipogenic and proliferation-related gene expression were conducted by qPCR and Western blot. MiR-130b overexpression markedly stimulated proliferation of PIMPA by increasing cell cycle-related gene expression. Furthermore, overexpression of miR-130b significantly inhibited adipogenic differentiation of PIMPA, mainly by inhibiting expression of adipogenic differentiation marker genes PPAR-γ and SREBP1. In addition, we proved that miR-130b significantly inhibited expression of PPAR-γ downstream target genes and ultimately repressed adipogenesis. Ssc-miR-130b accelerated proliferation but inhibited adipogenic differentiation of PIMPA, contributing to an enhanced knowledge of the function of ssc-miR-130b in lipid deposition, and providing potential implications for enhancing pork quality. Full article

Fat grafting is widely used in plastic surgery to correct soft tissue deformities. A major limitation of this technique is the poor long-term volume retention of the injected fat due to tissue remodeling and adipocyte death. To address this issue, various optimizations of the grafting process have been proposed. This scoping review focuses on preclinical and clinical studies that investigated the impact of various classes of soluble molecules on fat grafting outcomes. Globally, we describe that these molecules can be classified as acting through three main mechanisms to improve graft retention: supporting adipogenesis, improving vascularization, and reducing oxidative stress. A variety of 18 molecules are discussed, including insulin, VEGF, deferoxamine, botulinum toxin A, apocynin, N-acetylcysteine, and melatonin. Many biomolecules have shown the potential to improve long-term outcomes of fat grafts through enhanced cell survival and higher volume retention. However, the variability between experimental protocols, as well as the scarcity of clinical studies, remain obstacles to clinical translation. In order to determine the best preconditioning method for fat grafts, future studies should focus on dosage optimization, more sustained delivery of the molecules, and the design of homogenous experimental protocols and specific clinical trials. Full article

This study introduces a deep learning-based strategy to automatically detect the L3 slice and segment abdominal tissues from computed tomography (CT) images. Accurate measurement of muscle and fat composition at the L3 level is critical as it can serve as a prognostic biomarker for cancer diagnosis and treatment. However, current manual approaches are time-consuming and prone to class imbalance, since L3 slices constitute only a small fraction of the entire CT dataset. In this study, we propose an optimization-incorporated strategy that integrates augmentation ratio and class weight adjustment as correction design variables within deep learning models. In this retrospective study, the CT dataset was privately collected from 150 prostate cancer and bladder cancer patients at the Department of Urology of Gangneung Asan Hospital. A ResNet50 classifier was used to detect the L3 slice, while standard Unet, Swin-Unet, and SegFormer models were employed to segment abdominal tissues. Bayesian optimization determines optimal augmentation ratios and class weights, mitigating the imbalanced distribution of L3 slices and abdominal tissues. Evaluation of CT data from 150 prostate and bladder cancer patients showed that the optimized models reduced the slice detection error to approximately 0.68 ± 1.26 slices and achieved a Dice coefficient of up to 0.987 ± 0.001 for abdominal tissue segmentation-improvements over the models that did not consider correction design variables. This study confirms that balancing class distribution and properly tuning model parameters enhances performance. The proposed approach may provide reliable and automated biomarkers for early cancer diagnosis and personalized treatment planning. Full article

Anthocyanins, crucial flavonoids in plants, enhance stress tolerance in alfalfa and are attracting attention due to their antioxidant properties. This study analyzed red- and green-stemmed alfalfa using spectrophotometry, frozen sections, and LC-MS/MS. Anthocyanins were concentrated in stem vascular cambium, with red stems peaking at 61.08 mg g⁻¹ DW during the bud stage. Seven anthocyanidins were identified, with their corresponding aglycones including cyanidin, peonidin, and malvidin. At early flowering, red-stemmed alfalfa contained 35 classes of anthocyanins compared to 17 in green-stemmed varieties, with cyanidin-3-O-glucoside levels significantly higher in red stems (4.423 μg g⁻¹, p < 0.05). Red-stemmed alfalfa also showed higher contents of acid detergent fiber, crude fat, Cu, Fe, and Zn (p < 0.05), especially Zn (p < 0.01). Correlation analysis revealed a strong positive link between cyanidin and crude fat (Spearman’s ρ = 0.93, p < 0.01) and a significant negative correlation with neutral detergent fiber (ρ = −0.88, p < 0.05). Cyanidin and peonidin are associated with stem color differentiation, with cyanidin contributing predominantly to red pigmentation, whereas zinc and crude fat exhibit a synergistic correlation with anthocyanin accumulation. These findings may inform breeding strategies to develop anthocyanin-enriched alfalfa. Full article

Background/Objectives: Adherence to treatment instructions is essential in managing chronic diseases related to obesity. One gene associated with adherence is the serotonin transporter (5-HTTLPR) gene, which has long (L) and short (S) alleles, resulting in LL, SL, and SS genotypes. Risk alleles for obesity include the R variant of the β3-adrenergic receptor (β3AR) and the G variant of uncoupling protein 1 (UCP1). This study aimed to evaluate whether the S/L variant of 5-HTTLPR, the R variant of β3AR, and the G variant of UCP1 are associated with adherence to a weight loss program. To assess the factors influencing adherence, eating behavior was evaluated using the Eating Behavior Questionnaire (EBQ). Methods: This study included 56 well-educated and middle-class women with a mean age of 57.3 ± 10 years and a mean BMI of 27.2 ± 5.6 kg/m². Long-read sequencing was used to analyze S/L mutations. Participants followed a six-month diet and exercise regimen for obesity management. Outcomes were assessed using clinical data and EBQ scores. Adherence was objectively measured by the reduction in body fat percentage. Results: Participants were classified as SS (69.6%), SL (17.9%), or LL (12.5%). The R variant of β3AR was present in 34% of participants, with the G variant of UCP1 in 75%. After the intervention, SS participants showed significantly greater reductions in weight and body fat percentage than LL participants (p < 0.05). Among EBQ items, significant improvements (p < 0.05) were observed in SS participants for eating as a diversion, feeling of fullness, bad eating habits, unsteady eating patterns, and total EBQ score. In SL participants, only bad eating habits improved, whereas no significant changes were observed in LL participants. Obesity risk alleles did not significantly affect clinical outcomes, though there may be small number bias. Conclusions: SS genotype participants demonstrated higher adherence to the weight loss program, leading to improved clinical outcomes and EBQ scores, independent of obesity risk genes. Full article

Advanced glycation end products (AGEs) represent a class of toxic and irreversible compounds formed through non-enzymatic reactions between proteins or lipids and carbonyl compounds. AGEs can arise endogenously under normal metabolic conditions and in pathological states such as diabetes, kidney disease, and inflammatory disorders. Additionally, they can be obtained exogenously through dietary intake, particularly from foods high in fat or sugar, as well as grilled and processed items. AGEs accumulate in various organs and have been increasingly recognized as significant contributors to the progression of numerous diseases, particularly kidney disease. As the kidney plays a crucial role in AGE metabolism and excretion, it is highly susceptible to AGE-induced damage. In this review, we provide a comprehensive discussion on the role of AGEs in the onset and progression of various kidney diseases, including diabetic nephropathy, chronic kidney disease, and acute kidney injury. We explore the potential biological mechanisms involved, such as AGE accumulation, the AGEs-RAGE axis, oxidative stress, inflammation, gut microbiota dysbiosis, and AGE-induced DNA damage. Furthermore, we discuss recent findings on the metabolic characteristics of AGEs in vivo and their pathogenic impact on renal function. Additionally, we examine the clinical significance of AGEs in the early diagnosis, treatment, and prognosis of kidney diseases, highlighting their potential as biomarkers and therapeutic targets. By integrating recent advancements in AGE research, this review aims to provide new insights and strategies for mitigating AGE-related renal damage and improving kidney disease management. Full article