Search Results (13,753)

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and its early detection can be critical for a prompt intervention that can potentially slow down the disease progression and improve the patient’s quality of life. However, a diagnosis based solely on clinical symptoms can be challenging, especially in the early stages, while the detection of specific biomarkers such as amyloid-β peptide (Aβ) and tau proteins can provide objective evidence for diagnosis. In this work, we explored the effects of Aβ peptide on cell membrane properties thanks to fluorescence lifetime imaging (FLIM) combined with the phasor analysis (FLIM-ϕ). The results showed that the membrane viscosity is altered by the presence of Aβ peptide and that cells experience this effect even at nanomolar concentrations of peptide. This considerable sensitivity opens up the possibility of envisioning a cell-based biosensor able to detect very low concentrations of Aβ in a biological fluid, thus enabling timely diagnosis and intervention. Full article

Background/Objectives: Despite the growing popularity of large language models (LLMs), there remains a notable lack of research examining their role in workplace management. This study aimed to address this gap by evaluating the performance of ChatGPT-3.5, ChatGPT-4.0, Gemini, and Gemini Advanced as famous LLMs in responding to workplace management questions specific to radiology. Methods: ChatGPT-3.5 and ChatGPT-4.0 (both OpenAI, San Francisco, CA, USA) and Gemini and Gemini Advanced (both Google Deep Mind, Mountain View, CA, USA) generated answers to 31 pre-selected questions on four different areas of workplace management in radiology: (1) patient management, (2) imaging and radiation management, (3) learning and personal development, and (4) administrative and department management. Two readers independently evaluated the answers provided by the LLM chatbots. Three 4-point scores were used to assess the quality of the responses: (1) overall quality score (OQS), (2) understandabilityscore (US), and (3) implementability score (IS). The mean quality score (MQS) was calculated from these three scores. Results: The overall inter-rater reliability (IRR) was good for Gemini Advanced (IRR 79%), Gemini (IRR 78%), and ChatGPT-3.5 (IRR 65%), and moderate for ChatGPT-4.0 (IRR 54%). The overall MQS averaged 3.36 (SD: 0.64) for ChatGPT-3.5, 3.75 (SD: 0.43) for ChatGPT-4.0, 3.29 (SD: 0.64) for Gemini, and 3.51 (SD: 0.53) for Gemini Advanced. The highest OQS, US, IS, and MQS were achieved by ChatGPT-4.0 in all categories, followed by Gemini Advanced. ChatGPT-4.0 was the most consistently superior performer and outperformed all other chatbots (p < 0.001–0.002). Gemini Advanced performed significantly better than Gemini (p = 0.003) and showed a non-significant trend toward outperforming ChatGPT-3.5 (p = 0.056). ChatGPT-4.0 provided superior answers in most cases compared with the other LLM chatbots. None of the answers provided by the chatbots were rated “insufficient”. Conclusions: All four LLM chatbots performed well on workplace management questions in radiology. ChatGPT-4.0 outperformed ChatGPT-3.5, Gemini, and Gemini Advanced. Our study revealed that LLMs have the potential to improve workplace management in radiology by assisting with various tasks, making these processes more efficient without requiring specialized management skills. Full article

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture. Full article

Introduction: The highly contagious 2019 novel coronavirus that causes coronavirus disease 2019 increased the scientific community’s interest in diagnosing and monitoring COVID-19. Due to the findings about the association between COVID-19 infection and pulmonary disturbances, the need for the use of complementary tests that can be carried out, preserving the health of patients, has grown. In this context, single-photon emission computed tomography (SPECT) was performed during the COVID-19 pandemic to assess and try to diagnose lung lesions. The aim of this current review was to investigate the types of SPECT images most commonly used and the main pulmonary parenchymal lesions and different lung perfusion abnormalities observed in these images in individuals with COVID-19 in different countries in the world. Materials and Methods: Electronic searches in the MEDLINE/PubMed, Embase, Scopus, Web of Science, and CINAHL databases were conducted in December 2022. Studies that used SPECT/CT scans to evaluate pulmonary involvements due to COVID-19, with no language restriction, were included. Two reviewers, who independently examined titles and abstracts, identified records through the database search and reference screening, and irrelevant studies were excluded based on the eligibility criteria. Relevant complete texts were analyzed for eligibility, and all relevant studies were included in a systematic review. Results: Eight studies with regular methodological quality were included. The types of SPECT examinations used in the included articles were SPECT/CT, Q SPECT/CT, and V/Q SPECT. The possible pulmonary complication most observed was pulmonary embolism. Conclusion: This systematic review demonstrated that SPECT/CT scans, mainly with perfusion methods, allow the maximum extraction of benefits from pulmonary images, in safety, suggesting efficiency in the differential diagnosis, including of respiratory diseases of different etiology, and with diagnostics and additional analyses, can possibly aid the development of suitable therapeutic strategies for each patient. Randomized clinical trials and studies of good methodological quality are necessary to confirm the findings of this review and help better understand the types of SPECT images most commonly used and the main pulmonary parenchymal lesions observed in the images in individuals with COVID-19. Full article

Introduction: This narrative review aims to investigate the intricacy of human sexuality, the prevalence and effect of pelvic organ prolapse (POP) repair on overall sexual function and dyspareunia, and the subsequent repercussions on body image self-perception and quality of life. Methods: A MEDLINE and PUBMED search was conducted for studies evaluating the effect of POP surgery on sexual function and dyspareunia in sexually active women as well as its impact on body image self-perception and QoL. We included both observational and randomized controlled studies evaluating this subject. We evaluated patients who underwent anterior and/or posterior compartment repair eventually including vaginal hysterectomy. We excluded studies including women with concomitant anti-incontinence surgical correction and/or any vaginal reconstruction with synthetic materials. Results: Women with POP are more likely to diminish sexual activity due to a perceived impact on body image and attractiveness as well as worry of incontinence. Conservative management (such as pelvic floor muscle physiotherapy or pessary use) or surgical intervention via transabdominal or transvaginal routes have been used to treat POP, but concerns remain regarding sexual consequences. Despite a post-surgical positive sexual outcome, there is an inherent risk of de novo dyspareunia regardless of the surgical technique employed with slightly higher risk for the transvaginal approach. Patient counselling prior to surgery has proved to be an important element of POP treatment. Only studies on complications of POP surgery, specifically its impact on female sexuality, dyspareunia, global quality of life, and self-perceived body image, were included and analyzed for this review. We limited our search to the international English language literature published over the last three decades and excluded all studies involving the use of synthetic material in transvaginal POP repair. Discussion and Conclusions: Although no consistent evidence was found that disorders of the pelvic floor in women have a clear adverse effect on sexuality, their anatomical correction using the patient’s native tissues is recommended. Dyspareunia reduced significantly after repair, but the rate remains higher after the transvaginal approach versus the minimally invasive (robot-assisted and laparoscopic) approach used for sacrocolpopexy. Full article

Driven by the increasing awareness of environmental protection and the demand for personalized fashion, China’s second-hand clothing market is developing rapidly. Chinese consumers have begun to accept second-hand clothing, and online platforms such as Xianyu and Zhier have promoted the widespread trading of second-hand clothing. This study explored the key factors influencing customer satisfaction in China’s sustainable second-hand clothing market. Using a mixed research approach, factors such as pricing strategy, product quality, brand image, customer service, market environment and promotions were identified. The conclusion of grounded theory is that price, product quality, brand reputation, customer service quality, economic environment and platform promotions have a strong impact on customer satisfaction. The Kano model highlights the sensitivity of customer service quality, economic environment and promotions in improving satisfaction. Price is crucial, confirming the price sensitivity of customers. Brand reputation and product quality significantly increase satisfaction. Customer satisfaction significantly affects the amount of sustainable recycling. This study improves the theoretical framework and research hypotheses, provides valuable insights for future research and practical applications and contributes to the sustainable development of the second-hand clothing market. Full article

Landscape visual evaluation is a key method for assessing the value of visual landscape resources. This study aims to enhance the visual environment and sensory quality of urban landscapes by establishing standards for the visual comfort of urban natural landscapes. Using line-of-sight and multi-factor analysis algorithms, the method assesses spatial visibility and visual exposure of building clusters in the core urban areas of Harbin, identifying areas and viewpoints with high visual potential. Focusing on the viewpoints of landmark 3D models and the surrounding landscape’s visual environment, the study uses the city’s sky, greenery, and water features as key visual elements for evaluating the comfort of urban natural landscapes. By integrating GIS data, big data street-view photos, and image semantic recognition, spatial analysis algorithms extract both objective and subjective visual values at observation points, followed by mathematical modeling and quantitative analysis. The study explores the coupling relationship between objective physical visual values and subjective perceived visibility. The results show that 3D visual analysis effectively reveals the relationship between landmark buildings and surrounding landscapes, providing scientific support for urban planning and contributing to the development of a more distinctive and attractive urban space. Full article

This paper presents an integrated chaos-based algorithm for image encryption that combines the chaotic Hénon map and chaotic logistic map (CLM) to enhance the security of digital image communication. The proposed method leverages chaos theory to generate cryptographic keys, utilizing a 1D key from the logistic map generator and a 2D key from the chaotic Hénon map generator. These chaotic maps produce highly unpredictable and complex keys essential for robust encryption. Extensive experiments demonstrate the algorithm’s resilience against various attacks, including chosen-plaintext, noise, clipping, occlusion, and known-plaintext attacks. Performance evaluation in terms of encryption time, throughput, and image quality metrics validates the effectiveness of the proposed integrated approach. The results indicate that the chaotic Hénon–logistic map integration provides a powerful and secure method for safeguarding digital images during transmission and storage with a key space that reaches up to $2^{200}$. Moreover, the algorithm has potential applications in secure image sharing, cloud storage, and digital forensics, inspiring new possibilities. Full article

Potato is the fourth largest food crop in the world. Disease is an important factor restricting potato yield. Disease detection based on deep learning has strong advantages in network structure, training speed, detection accuracy, and other aspects. This article took potato leaf diseases (early blight and viral disease) as the research objects, collected disease images to construct a disease dataset, and expanded the dataset through data augmentation methods to improve the quantity and diversity of the dataset. Four classic deep learning networks (VGG16, MobilenetV1, Resnet50, and Vit) were used to train the dataset, and the VGG16 network had the highest accuracy of 97.26%; VGG16 was chosen as the basic research network. A new, improved algorithm, VGG16S, was proposed to solve the problem of large network parameters by using three improvement methods: changing the network structure of the VGG16 network from “convolutional layer + flattening layer + fully connected layer” to “convolutional layer + global average pooling”, integrating CBAM attention mechanism, and introducing Leaky ReLU activation function for learning and training. The improved VGG16S network has a parameter size of 15 M (1/10 of VGG16), and the recognition accuracy of the test set is 97.87%. This article used response surface analysis to optimize hyperparameters, and the test results indicated that VGG16S, after hyperparameter tuning, had further improved its diagnostic performance. At last, this article completed ablation experiments and public dataset testing. The research results will provide a theoretical basis for the timely adoption of corresponding prevention and control measures, improving the yield and quality of potatoes and increasing economic benefits. Full article

With recent electronic devices relying on sub-nanometer features, the understanding of device performance requires a direct probe of the atomic arrangement. As an ideal tool for crystallographic analysis at the nanoscale, aberration-corrected transmission electron microscopy (ACTEM) has the ability to provide atomically resolved images and core-loss spectra. Herein, the techniques for crystallographic structure analysis based on ACTEM are reviewed and discussed, particularly ACTEM techniques for measuring strain, dislocations, phase transition, and lattice in-plane misorientation. In situ observations of crystal evolution during the application of external forces or electrical fields are also introduced, so a correlation between crystal quality and device performance can be obtained. Full article

Breast ultrasound elastography phantoms are valued for their ability to mimic human tissue, enabling calibration for quality assurance and testing of imaging systems. Phantoms may facilitate the development and evaluation of ultrasound techniques by accurately simulating the properties of breasts. However, selecting appropriate tissue-mimicking materials for realistic and accurate ultrasound exams is crucial to ensure the ultrasound system responds similarly to real breast tissue. We conducted a systematic review of the PubMed, Scopes, Embase, and Web of Sciences databases, identifying 928 articles in the initial search, of which 19 were selected for further evaluation based on our inclusion criteria. The chosen article focused on tissue-mimicking materials in breast ultrasound elastography phantom fabrication, providing detailed information on the fabrication process, the materials used, and ultrasound and elastography validation of phantoms. The phantoms fabricated from Polyvinyl Chloride Plastisol, silicon, and paraffin were best suited for mimicking breast, fatty, glandular, and parenchyma tissues. Adding scatterers to these materials facilitates accurate fatty and glandular breast tissue simulations, making them ideal for ultrasound quality assurance and elastography training. Future research should focus on developing more realistic phantoms for advanced medical training, improving the practice of difficult procedures, enhancing breast cancer detection research, and providing tailored tissue characteristics. Full article

Aims: Ulcerative colitis (UC) significantly impacts individuals’ self-perception, body image, and overall quality of life, while also imposing considerable economic costs. These challenges highlight the necessity for complementary therapeutic strategies with reduced adverse effects to support conventional pharmacological treatments. Among natural interventions, Montmorency tart cherries, noted for their high anthocyanin content have emerged as a natural anti-inflammatory agent for UC. The current trial aimed to investigate the effects of Montmorency tart cherries compared to placebo in patients with mild to moderate UC. Materials and methods: Thirty-five patients with UC were randomly assigned to receive either placebo or Montmorency tart cherry juice, of which they drank 60 mL per day for 6 weeks. The primary outcomes and health-related quality of life, measured via the Inflammatory Bowel Disease Quality of Life Questionnaire (IBDQ), and the secondary measures, including other health-related questionnaires, blood biomarkers, and faecal samples, were measured before and after the intervention. Linear mixed-effects models were adopted to contrast the changes from baseline to 6 weeks between trial arms. Effect sizes were calculated using Cohen’s d. Results: There were significantly greater improvements in the IBDQ (22.61 (95% CI = 5.24 to 39.99) d = 0.90) and simple clinical colitis activity index (−3.98 (95% CI = −6.69 to –1.28) d = −1.01) in the tart cherry trial arm compared to placebo. In addition, reductions in faecal calprotectin levels were significantly greater in the tart cherry trial arm compared to placebo (−136.17 µg/g (95% CI = −258.06 to –4.28) d = −1.14). Loss to follow-up (N = 1) and adverse events (N = 1) were low and compliance was very high in the tart cherry (95.8%) trial arm. Conclusions: Given the profoundly negative effects of UC on health-related quality of life and its fiscal implications for global healthcare systems, this trial indicates that twice-daily tart cherry supplementation can improve IBD-related quality of life as well as the severity of symptoms and therefore may be important in the management of UC. Full article

Background: Radiomics has become a valuable tool in medical imaging, but its clinical use is limited by data variability and a lack of reproducibility between centers. This study aims to assess the differences between two scanners and provide guidance on image acquisition methods to reduce variations between images obtained from different centers. Methods: This study utilized medical images obtained in two different imaging centers, with two different 3T MRI scanners. For each scanner, 3D T2 FLAIR sequences were acquired in two forms: the raw and the clinical practice images typically used in diagnostic workflows. The differences between images were analyzed regarding resolution, SNR, CNR, and radiomic features. To facilitate comparison, bias field correction was applied, and the data were standardized to the same scale using Z-score normalization. Descriptive and inferential statistical methods were used to analyze the data. Results: The results show that there are significant differences between centers. Filtering and zero-padding significantly influence the resolution, SNR, CNR values, and radiomics features. Applying Z-score normalization has resolved variations in features sensitive to scale differences, but features reflecting dispersion and extreme values remain significantly different between scanners. Some feature differences may be resolved by analyzing the raw images in both centers. Conclusions: Variations arise due to different acquisition parameters and the differing quality and sensitivity of the equipment. In multi-center studies, acquiring raw images and then applying standardized post-processing methods across all images can enhance the robustness of results. This approach minimizes technical differences, and preserves the integrity of the information, reflecting a more accurate representation of reality and contributing to more reliable and reproducible findings. Full article

Pickering emulsions (PEs) can be utilized as inks for 3D food printing owing to their extensive stability and appropriate viscoelastic properties. This research explores food-grade PEs stabilized with nanoparticles (NPs) based on modified pea protein (PP) isolate and k-carrageenan (KC). NPs are fabricated from solutions with different concentrations of protein and polysaccharide and characterized in terms of size, zeta potential, and wetting properties. The composition of the emulsion is 60% sunflower oil and 40% aqueous phase. Nine emulsion formulations with varying PP and KC concentrations are investigated. The formation of hollow NPs with a hydrodynamic diameter of 120–250 nm is observed. Microscope imaging shows oil droplets surrounded by a continuous aqueous phase, forming homogenous PEs in all formulations that are stable for over 30 days. Further, the oil droplet size decreases with increasing NP concentration while the viscosity increases. Rheologic experiments portray elastic emulsion gels with thixotropic qualities ascribed to the presence of the polysaccharide. The emulsions are subjected to centrifugation in order to compare the original emulsions to concentrated PEs that possess improved capabilities. These emulsions may serve as sustainable and printable saturated fat alternatives due to their composition, texture, stability, and rheological properties. Lastly, PEs are printed smoothly and precisely while maintaining a self-supported structure. Full article

Modulus of elasticity (MOE) and modulus of rupture (MOR) are crucial indicators for assessing the application value of wood. However, traditional physical testing methods for the mechanical properties of wood are typically destructive, costly, and time-consuming. To efficiently assess these properties, this study proposes a multi-input residual network (MIRN) model, which integrates microscopic images of wood with physical density data and leverages deep learning technology for rapid and accurate predictions. By using larger convolution kernels to enhance the receptive field, the model captures fine microstructural features in the images. Batch normalization layers were removed from the ResNet architecture to reduce the number of parameters and improve training stability. Shortcut connections were utilized to enable deeper network architectures and address the vanishing gradient problem. Two types of residual blocks, convolutional block and identity block, were defined based on input dimensional changes. The MIRN method, based on multi-input residual networks, is proposed for non-destructive testing of wood mechanical properties. The experimental results show that MIRN outperforms convolutional neural networks (CNNs) and ResNet-50 in predicting MOE and MOR, with an R² of 0.95 for MOE and RMSE reduced to 46.88, as well as an R² of 0.85 for MOR and an RMSE of 0.44. Thus, this method offers an efficient and cost-effective tool for wood processing and quality control. Full article