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Navigating the Challenges: A Commentary on Barriers to Autism Screening in Childcare Centers
No full textAlthough the American Academy of Pediatrics has long recommended universal autism-specific screening at well-child pediatric visits, implementation challenges in primary care settings interfere with high-fidelity universal autism screening. These challenges delay autism identification for some children, leading to delays in needed services and supports. Prior findings indicate that new solutions must be developed to bridge the gap in access to autism screening for families, particularly among those who are under-resourced. One approach is expanding screening to other community settings, such as childcare centers, but there are barriers to this approach, which this commentary aims to address. We discuss challenges and barriers in childcare screening identified through our recently completed pilot study screening for autism in childcare centers, with suggested strategies to address them. These challenges include hesitation among childcare staff to guide conversations or concerns about autism, and stigma around autism diagnosis and presentation. Other challenges relate to emerging concerns regarding legal, ethical, and professional roles and responsibilities surrounding informed consent and data privacy, as well as the identification of children without timely follow-up evaluation and services. There is a need for increasing public awareness as an essential component of autism screening across settings. Our commentary discusses different considerations and practice strategies to meet these needs
Oxidative-Stress-Mediated AMPK/mTOR Signaling in Bovine Mastitis: An Integrative Analysis Combining 16S rDNA Sequencing and Molecular Pathology
No full textThe bovine mammary gland, the exclusive site of milk synthesis, is a structurally specialized tissue that houses distinct cellular subsets, yet it remains highly susceptible to major mastitis pathogens, including Staphylococcus aureus, Streptococcus agalactiae, and Escherichia coli. Infection disrupts redox homeostasis, leading to excessive accumulation of reactive oxygen species (ROS) and rapid activation of antioxidant pathways. In this study, we integrated 16S DNA sequencing, histopathology (hematoxylin and eosin), and immunohistochemistry to map the mastitis-associated microbiota and visualize oxidative-damage foci in mammary tissues challenged by Staphylococcus aureus, Streptococcus agalactiae, or Escherichia coli. Quantitative reverse transcription polymerase chain reaction and Western blot analyses were subsequently performed on the same samples to measure the kinetic response of six oxidative-stress-related signalling nodes: adenosine 5′-monophosphate-activated protein kinase, cytochrome P450 1A1, heme oxygenase 1, nitric oxide synthase, mammalian target of rapamycin, and superoxide dismutase. By correlating the temporal expression patterns of these genes/proteins with ROS accumulation and histological severity, this study delineates the molecular cascade linking oxidative imbalance to mastitis pathology, providing data-driven targets for future preventive and therapeutic strategies
A Supervised Deep Learning Model Was Developed to Classify Nelore Cattle (Bos indicus) with Heat Stress in the Brazilian Amazon
No full textNon-invasive and intelligent technologies have been utilized to monitor agricultural systems in real time, facilitating expedient decision-making and the reduction in animal stress in diverse climatic conditions. The objective of this study was to develop a deep learning supervised model to classify Nelore cattle (Bos indicus) into two groups: those in comfort and those under thermal stress. Thirty cattle, aged between 18 and 20 months, were evaluated between June and December 2023, resulting in 676 samples collected across four daily periods (6:00, 12:00, 18:00, and 24:00). Biotic variables included rectal temperature (RT) and respiratory rate (RR), while abiotic variables included air temperature (AT) and relative humidity (RH). The neural network model exhibited an accuracy and recall of 72% but a low specificity of 42%. These metrics indicate that while the model is effective in detecting stress situations, it faces challenges in correctly identifying animals in thermal comfort, likely due to class imbalance and the need for additional input features to capture environmental adaptability. Consequently, it can be posited that supervised learning models are valuable tools for precision livestock farming, provided that discriminatory limitations are mitigated by refining input characteristics and data balancing
Advancing Concession-Scale Carbon Stock Prediction in Oil Palm Using Machine Learning and Multi-Sensor Satellite Indices
No full textReliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree−1) and hectare (CO_ha, Mg C ha−1) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R2 values of 0.86–0.88 and RMSE of 59–64 kg tree−1 or 8–9 Mg ha−1. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree−1 and CO_ha from 20–70 Mg C ha−1, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting
From Hero to Hijacker: Autophagy’s Double Life in Immune Patrols and Cancer Escape
No full textCells are constantly exposed to mechanical forces that shape their behavior, survival, and fate. The autophagy machinery emerges as a central adaptive pathway in these processes, acting not only as a metabolic and quality control mechanism but also as a key regulator of membrane dynamics and mechanotransduction. Here, we review how mechanical stress influences autophagy initiation, autophagosome maturation, and lysosomal function across different cell types. We discuss parallels between leukocyte diapedesis and circulating tumor cell (CTC) extravasation, two processes that involve profound mechanical challenges and rely on autophagy-related pathways to maintain cell integrity and enable transendothelial migration. Special attention is given to the dual role of autophagy-related proteins (ATGs) in these contexts, ranging from cytoplasmic degradation dependent on lysosomal fusion to secretory functions. Understanding how mechanical forces modulate autophagy and ATG-dependent pathways may reveal novel insights into immune regulation, tumor dissemination, and potential therapeutic targets aimed at controlling inflammation and metastasis
Cholesterol Metabolism: An Ally in the Development and Progression of Cervical Cancer
No full textDespite screening programs and vaccination campaigns, cervical cancer (CC) remains a health problem worldwide. The involvement of the E6 and E7 oncoproteins of Human Papillomavirus (HPV) is crucial for the development and progression of this type of cancer. Metabolic reprogramming by cancer cells has gained relevance in the last decade due to its ability to promote cell growth, survival, invasion, metastasis, and resistance to therapy. In this review, we focus on alterations in cholesterol metabolism that significantly influence the development and progression of CC, as well as the clinical outcome of patients. Furthermore, evidence from comprehensive omics studies suggesting that E6 and E7 are involved in the exacerbation of elements related to cholesterol metabolism is analyzed. Preclinical and clinical studies are also discussed that demonstrate that cholesterol metabolism is a potential therapeutic target, highlighting its impact on reducing tumor growth, altering the tumor microenvironment, and improving antitumor immunity
The Associations Between Participation in Leisure Dance Activity, Perceived Health Status, Happiness Level, and Perceptions of Leisure Amidst Selected Demographic Determinants
No full textObjectives/Background: In this study, we aimed to examine the associations of participation in leisure dance activity with perceived health status, happiness level, and perceptions of leisure, and the relevance of demographic characteristics to these associations. Moreover, we sought to explore ways of revitalizing leisure dance activity. Methods: We used data from the 2022 Korea National Leisure Activity Survey organized by the Korean Ministry of Culture, Sports, and Tourism. The 2022 Korea National Leisure Activity Survey was conducted from September to November 2022. The survey was conducted among 10,046 Koreans aged ≥15 who lived in 17 cities and provinces across the country. The participants were informed about the survey schedule in advance, and interviews were conducted at the participants’ homes using tablet PCs. Random telephone verification was performed on the recovered questionnaires to ensure the accuracy of responses. Questionnaires that passed the first verification were subjected to secondary verification by a computerized program, and questionnaires that did not pass the verification were supplemented and re-examined. The collected data were entered electronically through an encoding process, and only the data that passed the final test were compiled in the multi-stage verification process. The data were analyzed using frequency analysis, chi-squared tests, and multivariate logistic regression analysis. Results: Of the 1004 participants, 655 (6.5%) participated in leisure dance activity. Women, individuals in their twenties and thirties, college graduates or those with lower-level educational qualifications, and unmarried individuals were more likely to participate in leisure dance activities. In addition, leisure dance activities were found to be likely to increase happiness levels and leisure life satisfaction, and leisure expenses were likely to be low. Conclusions: Schools should provide high-quality dance education and enhance the professionalism of physical education teachers in the management of dance classes. This is because dance-related experiences in childhood can increase the likelihood of engaging in dancing in adulthood. Efforts are also needed to increase men’s awareness of and participation in leisure dance activities and to lower barriers to entry. The convergence of dance, games, and technology can make this possible
Aster pekinensis Extract Mitigates High-Fat-Diet-Induced Obesity and Metabolic Dysfunction in Mice
No full textObesity and metabolic disorders are an increasing concern in companion animals, creating demand for herb-derived nutraceuticals and functional feeds. This study evaluated whether a water extract of Aster pekinensis (AP) ameliorates high-fat-diet (HFD)-induced obesity and metabolic dysfunction in mice. The phytochemical profile of AP was characterized by mass spectrometry, revealing oleanane-type triterpenoid saponins and dicaffeoylquinic acids. Male C57BL/6 mice were fed an HFD and orally given AP (10–200 mg/kg/day) for 12 weeks, with normal diet and untreated HFD groups as controls. AP at 50–200 mg/kg/day reduced body-weight gain, adipose tissue mass and food efficiency without lowering food intake, and improved fasting glucose and atherogenic lipid indices. AP also enhanced glucose tolerance and insulin sensitivity, attenuated hepatic steatosis, hepatocellular ballooning, lobular inflammation and non-alcoholic fatty liver disease (NAFLD) Activity Score, and decreased serum liver enzyme activities. These effects were accompanied by modulation of hepatic genes involved in lipogenesis and inflammation. Together, these findings indicate that AP extract mitigates diet-induced obesity and NAFLD-like liver injury and supports further development as a herb-derived nutraceutical or functional feed ingredient for managing obesity-related metabolic disorders in companion animals
Atomic Bond Strain: A New Strain Measure Displaying Nearly Perfect Linear Correlation with Stress Throughout Plastic Deformation of Single-Crystal FCC Metals
No full textAtomic-scale strain is the basis of a material’s macroscopic deformation behavior. The current measure of atomic-scale strain in the form of the Green–Lagrange tensor loses its physical meaning beyond the yield point, as atomic neighborhoods undergo significant reconstructions. We have recently introduced a new atomic-scale strain measure, namely, atomic bond strain, through our study of bond behavior in multicomponent metallic glasses. Here, we apply this new strain measure to uniaxial tensile tests (simulated using molecular dynamics) of several representative single-crystal FCC (face-centered cubic) metals under varied strain rates. We show that this new strain measure displays remarkable near-linear correlation with stress, not only in the elastic regime, but also in the plastic regime where complex dislocation dynamics (nucleation, bursting, motion, annihilation, regeneration) and stress fluctuations take place. This suggests that the overall stress of the materials even in the plastic regime is predominantly determined by the degree of bond stretching among all atoms. This appears to contradict the common conceptions that the plastic flow stress of a crystalline material is governed by dislocation events involving only a small fraction of atoms around dislocations, and that the stress–strain relationship is highly non-linear for plastic deformation. The contradictions can be reconciled by considering the causal sequence: dislocation events alter bond stretching, and bond stretching directly determines the stress. This brings a novel insight into the nature of plastic deformation, owing to the newly introduced atomic bond strain. How well the near-linear correlation between the stress and the atomic bond strain holds in other materials (e.g., non-FCC single crystals, polycrystals, quasicrystals, elements, alloys, and compounds) is an intriguing and important topic for future investigation, following the example of this work
On Construction of Tibial Plateau Fracture Detection in Different Radiographic Views Using YOLO Models
No full textBackground/Objectives: Tibial plateau fractures are difficult to detect using X-ray imaging due to limited three-dimensional visibility. This study evaluated the performance of four You Only Look Once (YOLO) deep learning models trained on different radiographic views for fracture detection. Methods: A total of 1489 knee X-rays were collected from a tertiary referral hospital, comprising 727 fracture images and 762 non-fracture images. YOLOv4, YOLOv5, YOLOv8, and YOLOv9 were each trained using anteroposterior (AP), lateral, and combined views. Results: YOLO models trained on AP views consistently outperformed those trained on other views. YOLOv9 trained on AP images achieved the highest accuracy, specificity, precision, F1-score, and area under the curve (AUC) of 0.99, with both sensitivity and negative predictive value (NPV) at 1.00. YOLOv8 trained on AP views reached 0.97 across all metrics with an AUC of 0.98. YOLOv5 trained on AP images achieved an accuracy and F1-score of 0.98, a sensitivity and NPV of 0.97, and an AUC of 1.00. YOLOv4 trained on AP views showed slightly lower performance, with an accuracy and F1-score of 0.96 and an AUC of 1.00. External validation confirmed the strong generalizability of AP-trained models, particularly YOLOv9, which reached an accuracy of 0.87, a sensitivity of 1.00, a specificity of 0.75, a precision of 0.80, an NPV of 1.00, an F1-score of 0.88, and an AUC of 0.93. Artificial intelligence-assisted YOLO models showed strong potential in detecting tibial plateau fractures. Conclusions: Models trained on AP views consistently achieved better diagnostic accuracy than those using other views. Among all, YOLOv9 delivered the best results, highlighting the benefits of newer deep learning architectures