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BONE-Net: A novel hybrid deep-learning model for effective osteoporosis detection
Osteoporosis is a prevalent bone disease characterized by reduced bone density and an elevated risk of fractures, especially in older adults and postmenopausal women. The clinical consequences of osteoporotic fractures extend beyond pain and disability, contributing substantially to morbidity, mortality, and healthcare costs. Early intervention and accurate detection is therefore essential to improve patient outcomes. This paper introduces an advanced deep-learning methodology to enhance the accuracy and efficiency of osteoporosis detection through knee X-ray analysis. The proposed approach integrates features from two pre-trained models, DenseNet169 and Vision Transformer (ViT), with a custom-designed Attention Model (AM) to capture detailed spatial and channel-specific information from the input images. These fused features are then fed into a fully connected neural network to classify the images as osteoporotic or normal. The results indicate significant improvements in classification accuracy, achieving a high accuracy rate on previously unseen test data. The proposed model achieves superior performance over existing methods and other recent models for osteoporosis detection, with an accuracy of 0.8611, specificity of 0.9474, and precision of 0.9286. Our approach effectively combines convolutional and transformer-based representations, enabling extraction of both local and global features for comprehensive bone characterization. These findings highlight the model’s potential to support early diagnosis, timely intervention, and improved patient care in osteoporosis management.
AI’s therapeutic potential goes beyond emotional connection
Correction: Design of an EWMA control chart by adaptation of smoothing constant based on a function of estimated shift
Correction: The effect of dietary interventions on inflammatory biomarkers among people with multiple sclerosis: A protocol for systematic review and meta-analysis of randomized controlled trials
Tetrahydrobiopterin enhances regulatory T- and mast cell proliferation and alters cytokines expression in a murine heart transplant model
Abstract Administration of tetrahydrobiopterin (BH4) has been shown to attenuate acute allograft rejection in a murine heart transplantation model in a manner similar to that of cyclosporine A. However, its mechanism of action on immune cells remains largely unknown. A fully MHC-mismatched (C3H/He to C57BL/6) mouse heart transplant model was used in this study. The recipients were treated with BH4 or cyclosporine A six days. The degree of acute rejection was assessed by histopathological analysis, splenocytes were analyzed by flow cytometry, and cytokine production was estimated based on the level of protein and RNA in sera and grafts and in vitro in T cell cultures. Proliferation of regulatory T cells and mast cells, suppressor capacity of Tregs, and MLR of T cells were conducted in vitro. Survival curves confirmed the significant improvement observed in the BH4-treated animals. BH4-treatment resulted in a substantial increase in Tregs and mast cells in the secondary lymphoid organs. In vitro assays showed increased proliferation of BH4-treated Tregs and mast cells. Cytokine production in vivo and in vitro in BH4-treated animals revealed an increase in the expression of IL-10, IL-5 and IL-4. BH4-dependent mast cell-derived tryptophan hydroxylase-1 could be excluded as a treatment target in recipient knockout mice. These data suggest that BH4 modulates the innate and adaptive immune systems, resulting in increased proliferation of regulatory T and mast cells accompanied by a modulation of anti-inflammatory cytokines.
Co-designing accessible and inclusive patient information resources for gastrointestinal endoscopy using Patient and Public Involvement (PPI) and Universal Design for Learning (UDL) principles
Co-designing accessible and inclusive patient information resources for gastrointestinal endoscopy using Patient and Public Involvement (PPI) and Universal Design for Learning (UDL) Principles. Introduction Patients undergoing GI endoscopy can experience anxiety before their procedure, for numerous different reasons, including ineffective patient education resources received beforehand. Paper-based information leaflets are insufficient to accommodate for the diverse way in which people access, consume and process information. Public and patient involvement (PPI) and Universal Design for Learning (UDL) are two well-described pedagogical principles that strive to optimise patient-centred care and inclusivity. Objectives Our aim was to apply these principles to design more effective and accessible patient education materials, improving the health literacy of our patients. Working with patient partners, we identified the need to develop high-quality and trustable video resources for patients, that would be available on our hospital website. These videos were co-designed by patients and other key stakeholders. Results We used techniques such as storyboard development, the UDL educational principles of representation, engagement and expression, as well as the individual expertise of our stakeholder panel members to achieve appropriate and accessible information for our patient cohort. The development phase was an iterative process, with feedback and input from patient partners and other stakeholders playing a crucial role in prompting necessary adjustments for accuracy and patient understanding. Our project is the first guide in combining both PPI and UDL principles in the development of patient information and education materials. Conclusion By involving patients and other key stakeholders as partners, we improved the relevance and quality of our patient information content. Identification of patient partners and appropriate other stakeholders is an important initial step when co-designing patient information resources. The use of UDL in the co-design process allows for a structured approach to creating accessible content, highlighting important steps that otherwise may be overlooked by team members. Formal assessment of the impact of these co-designed videos, through quantitative and qualitative methods, will be assessed as part of a larger study.
A study on the promoting effect of environmental penalties on climate-friendly technological innovation in China
Arsenic and heavy metal contamination in drinking water from an industrial zone in Dhaka District, Bangladesh
Heavy metals (HMs), even in trace concentrations, can pose serious health risks when consumed over time. In Bangladesh, the widespread use of tube wells for drinking water, coupled with industrial activity, has contributed to the contamination of groundwater with HMs. This study investigated heavy metal contamination in drinking water samples from Gazipur, an industrial hub, using Inductively Coupled Plasma-mass Spectrometry (ICP-MS). The results revealed that the mean concentrations of HMs in mg/L followed the order: Fe (5.479 ± 3.740)> Mn (0.203 ± 0.233)> Pb (0.133 ± 0.370)> Zn (0.068 ± 0.070)> Cu (0.016 ± 0.034)> As (0.003 ± 0.004)> Ni (0.002 ± 0.001)> Cr (0.002 ± 0.001). Concentrations of Pb, Fe, and Mn exceeded safe limits while As, Cd, Cr, Ni, Cu, and Zn were within acceptable ranges. The scatter plot analysis revealed weak and non-significant correlations between As concentrations and other heavy metals with low R² values. A strong difference in metal contamination levels between shallow (20–80 meters) and deep wells (>80 meters), with shallow wells exhibiting significantly higher contamination percentages, often approaching 100%, while deep wells consistently remained below 30%. Ecological risk assessments showed low to moderate contamination at most sampling sites. Health risk evaluations (HQ and HI) indicated that all metals remained below harmful levels, though arsenic posed a heightened cancer risk, particularly for children. Principal Component Analysis (PCA) and Cluster Analysis suggested that As, Pb, Fe, Zn, and Mn were linked to industrial activities, while the other metals were likely of geological origin. The study emphasized the need for ongoing surveillance and intervention to protect public health in areas impacted by industrial pollution.
Brazil’s COP30 legacy should be to protect more of its forests
Comparative antioxidant activity of pomegranate leaf extract and derived metabolites in single and co-cultured cell lines
Unveiling hidden threats: Polycyclic aromatic hydrocarbons pollution in the glacial waters of the Meili Snow Mountains in the southeastern Tibetan Plateau
Polycyclic aromatic hydrocarbons (PAHs) have posed considerable threats to both ecosystems and human health. To explore their characteristics and risks in temperate glacial watersheds, water samples from the Meili Snow Mountains in the southeastern Tibetan Plateau were collected and analyzed. The results revealed that the concentrations of total PAHs (∑PAHs) ranged from 406.5 to 820.9 ng‧L−1, and the mean ∑PAH level was relatively high compared to other global studies. PAHs were characterized by low–molecular–weight congeners, mainly including fluorene, and phenanthrene. Fluorene, phenanthrene, pyrene, and benzo[a]pyrene, were prevalent throughout the Meili Snow Mountains, with concentrations ranging from 164 to 425 ng‧L−1, 23.6 to 201 ng‧L−1, 17.9 to 90.1 ng‧L−1, and 50–117 ng‧L−1, respectively. PAHs originated from heterogeneous combustion sources, such as coal combustion, vehicular emissions, and biomass burning. These variations were attributed to various factors, including altitude effects, long–range atmospheric transport, and local environmental driving patterns. Specifically, ∑PAHs in runoff rivers decreased with increasing altitude, reflecting differences in local anthropogenic activities. The risk assessment indicated that PAHs posed moderate to high ecological risks and potential carcinogenic threats. This study provides valuable insights into the safety of drinking surface water resources and the structural and functional stability of ecosystem in the Meili Snow Mountains, which is significant for improving regional ecological safety and human health.
Inside our university’s mission to pivot to research
Secure edge-based IoMT framework for ICU monitoring with TinyML and post-quantum cryptography
Open Science practices in a Portuguese nursing higher education institution: An exploratory, descriptive study
Open Science has emerged as an epistemological paradigm that promotes open and dynamic access to knowledge, fostering the sharing of scientific findings, collaboration between academia and society, and enhancing innovation and transparency. However, comprehensive studies on the adoption of Open Science practices remain limited, particularly in the nursing academic context. This study aimed to identify the current Open Science practices within a Portuguese higher nursing education institution community. An exploratory, descriptive study was conducted in two phases. In the first phase, a questionnaire was adapted and piloted through a focus group. In the second phase, the final version of the questionnaire, with 34 questions, was distributed digitally to 747 teachers, researchers and students. The study included 35 respondents, predominantly faculty members. While 77.8% reported engaging with Open Science publications, fewer respondents demonstrated familiarity with practices such as study preregistration and open-source coding. The principles of Open Science were widely accepted, particularly those emphasizing ethics, the democratization of knowledge, and scientific collaboration. Participants frequently used tools like EBSCOhost, Medline, and Scopus, whereas platforms such as Zenodo and OpenUP Hub remained underutilized. Participants highlighted training priorities, especially in open access publishing, data management, and the implementation of Open Science recommendations. The low response rate may indicate limited awareness of Open Science among professionals and an institutional culture in which such practices have not been fully embedded within academic routines. Although all participants agreed with the principles of Open Science, most reported not knowing its policies and not all were familiar with the various formats. These findings underscore the importance of investing in skills training and raising awareness of Open Science practices, particularly within the nursing school context. Furthermore, they underscore the need for in-depth investigation into the motivations, barriers, and enabling conditions that influence adherence to Open Science in higher education.
Efficient FPGA implementation of polar codes-based information reconciliation for quantum key distribution
A contrastive adversarial encoder for multi-omics data integration
Early and accurate cancer detection is crucial for effective treatment, prognosis, and the advancement of precision medicine. Analyzing omics data is vital in cancer research. While using a single type of omics data provides a limited perspective, integrating multiple omics modalities allows for a more comprehensive understanding of cancer. Current deep models struggle to achieve efficient dimensionality reduction while preserving global information and integrating multi-omics data. This often results in feature redundancy or information loss, overlooking the synergies among different modalities. This paper proposes a contrastive adversarial encoder (CAEncoder) for multi-omics data integration to address this challenge. The proposed model combines a Vision Transformer (ViT) and a CycleGAN, trained in an end-to-end contrastive manner. The ViT is the encoder, utilizing self-attention, while the CycleGAN employs adversarial learning to ensure more discriminative and invariant latent space embeddings. Contrastive adversarial training improves representation quality by preventing information loss, eliminating redundancy, and capturing the synergies among different omics modalities. To ensure contrastive adversarial training, a composite loss function is used, consisting of a weighted combination of Adversarial Loss (Hinge Loss), Cycle Consistency Loss, and Triplet Margin Loss. The Adversarial Loss and Cycle Consistency Loss provide feedback from the CycleGAN, ensuring effective adversarial learning. Meanwhile, the Triplet Margin Loss promotes contrastive learning by pulling similar samples together and pushing dissimilar samples apart in the latent space. The performance of the CAEncoder is evaluated on downstream classification tasks, including both binary and multi-class classifications of five different cancer types. The results show that the model achieved a classification accuracy of up to 93.33% and an F1 score of 92.81%, outperforming existing advanced models. These findings demonstrate the potential of our method to enhance precision medicine for cancer through improved multi-omics data integration.
Faulty mitochondria cause deadly diseases: fixing them is about to get a lot easier
High-pressure torsion of biodegradable Mg−Zn−Mn alloy and investigate mechanical and corrosion behaviour
Abstract Considering their biodegradability in physiological environments and similar elastic modulus to natural bone, magnesium alloys have generated a lot of interest as biodegradable implant materials. Their poor corrosion resistance is primarily a result of the inhomogeneous distribution of their second phase, which limits their clinical application. High pressure torsion (HPT) one of the severe plastic deformation techniques which provides an opportunity to process materials with low formability such as magnesium at room temperature. The present study HPT is conducted for Mg-Zn-Mn alloy up to ten revolutions at room temperature. Optical, scanning, and transmission electron microscopes were used to examine the microstructures of base material (BM) and ten revolution HPT samples. Significant microhardness improvement was observed in HPT N10 samples (222 Hv) as compared to BM samples (68 Hv). It was determined that the improvement in microhardness was primarily due to dislocation strengthening, fine grain strengthening, and second phase strengthening. Potentiodynamic polarisation and electrochemical impedance spectroscopy (EIS) were used in a simulated body fluid (SBF) solution to assess the corrosion behaviour. When compared to the BM sample (0.0243 mm/y), the corrosion resistance of the HPT N10 sample (0.0012 mm/y) increased significantly. This was mostly due to the smaller grain size and uniform dispersion of the secondary phases, which result in a uniform corrosion. Further, obtained data from the cytotoxicity assay carried out using the MTT method indicated the compatibility of the Mg-Zn-Mn alloy on MG-63 osteoblast-like cells, further substantiating its safety on the bone cells.
The impact of music education on children’s cognitive and socioemotional development: A quasi-experimental study in the Guri Program in Brazil
Background Transferability of music education to cognitive and social skills has recently been explored, but its causal effects remain debatable. Objectives To examine the impact of the Guri music education program on children’s cognitive and socioemotional skills in children from underserved communities in São Paulo, Brazil.