Browse Articles
Discover research articles across all indexed journals
Medical image segmentation by combining feature enhancement Swin Transformer and UperNet
Mindfulness training decreases the habituation response to persistent food stimulation
Abstract Modern societies and their obesogenic environments expose individuals to persistent food stimulation. This frequent exposure can cause sensory systems to habituate or desensitize to food-related sensory stimulation. This can, in turn, lead to the reduction of pleasure associated with eating, which can elicit overeating behavior to attain the desired pleasurable effect. However, frequently engaging in overeating behavior can lead to excessive weight gain, which is associated with the development of metabolic and cardiovascular disease. Mindfulness training could serve as a tool to reduce the habituation response elicited by frequent food exposure while promoting mindful eating, emotion regulation, and reducing overeating behavior. To investigate this, the present study was registered as a clinical trial on the ISRCTN registry: trial ID ISRCTN12901054. In the study, meditation-naïve individuals with a tendency to stress-eat (N = 56) participated in either a 31-day, web-based, food-related mindfulness training or health training condition. Functional magnetic resonance imaging (fMRI) and behavioral data were acquired before and after the intervention. During the fMRI sessions, hungry and stressed participants were exposed to visual and olfactory high-calorie food stimuli. The results indicate that hunger and stress ratings increased in both groups during the fMRI sessions but that mindfulness training, in comparison to health training, may significantly reduce the habituation response to food stimuli. Our results demonstrate that the habituation response could be implicated through the increase of neural activity in brain regions involved in visual and olfactory processing as well as emotion regulation. This study, therefore, demonstrates that mindfulness training could improve the ability to attend to food stimuli, which may enhance the pleasurable experience of eating, thereby diminishing an individual’s tendency to engage in overeating behavior.
In-situ XAFS measurements of amorphous Li3PO4-doped V2O5 cathode for all-solid-state thin-film Li-ion batteries
Exploring the nexus of industrial production and energy consumption on CO2 emissions in Bangladesh through ARDL bounds testing insights
No sex difference in the association of pre-stroke physical activity with functional independence after ischemic stroke
Author Correction: Mechanism of skull base osteoradionecrosis explored through laboratory assessment with propensity score-matched analysis
High precision experimentally validated adaptive neuro fuzzy inference system controller for DC motor drive system
The reliability of medical illness reporting in a randomized clinical trial
Background/Objective Reported medical disorders from population surveys, medical records, and clinical trials, may not be accurate and methods are needed to improve confirmation. We report the accuracy of reported prevalence of medical disorders in a clinical trial and comparison with potential verification methods. Methods We report the prevalence of 11 medical disorders, utilizing prospectively collected data from 729 participants in an eight-country multicenter clinical treatment trial on non-arteritic anterior ischemic optic neuropathy (NAION). We chose disorders where the medical history was potentially verifiable. We determined the prevalence using four methods: Method (M)1: Participant and medical health record reporting; M2: Physical examination, clinical tests; M3: Medication indications; M4: Combining M2 and M3. We estimated concordance between M1 and the other methods using Cohen’s kappa (K) statistic. Results Prevalence of the medical disorders based on M1 were lower than for either M2 or M3, depending on the disorder, and consistentlly lower for M4. For M1 and M4, moderate concordance (K ≥ 0.50) was observed only for psychiatric disorders (K = 0.52) and prior NAION (K=0.67). The prevalence and concordance for M1 and M4 for anemia, hypertension, diabetes and psychiatric disease were the only disorders that differed between females and males. For all methods, the prevalence varied widely across countries. Concordance for M1 and M4 varied and moderate concordance occurred for psychiatric disorders and prior NAION. Conclusion Even with prospective, rigorously collected data, medical histories do not reliably identify all medical disorders. Adding the results of physical examination, laboratory tests, and medications increases the accuracy of reporting. This strategy could be adapted for clinical trials and electronic medical record disease-prevalence data mining.
RETRACTED ARTICLE: A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection
Liquids in a glass recover a graceful shape even after being shaken
A dual-branch model combining convolution and vision transformer for crop disease classification
Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model for crop disease classification, which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). Here, the convolutional branch is utilized to capture the local features while the Transformer branch is utilized to handle global features. A learnable parameter is used to achieve a linear weighted fusion of these two types of features. An Aggregated Local Perceptive Feed Forward Layer (ALP-FFN) is introduced to enhance the model’s representation capability by introducing locality into the Transformer encoder. Furthermore, this paper constructs a lightweight Transformer block using ALP-FFN and a linear self-attention mechanism to reduce the model’s parameters and computational cost. The proposed model achieves an exceptional classification accuracy of 99.71% on the PlantVillage dataset with only 4.9M parameters and 0.62G FLOPs, surpassing the state-of-the-art TNT-S model (accuracy: 99.11%, parameters: 23.31M, FLOPs: 4.85G) by 0.6%. On the Potato Leaf dataset, the model attains 98.78% classification accuracy, outperforming the advanced ResNet-18 model (accuracy: 98.05%, parameters: 11.18M, FLOPs: 1.82G) by 0.73%. The model proposed in this paper effectively combines the advantages of CNN and ViT while maintaining a lightweight design, providing an effective method for the precise identification of crop diseases.
Urinary iodine levels and thyroid disorder prevalence in the adult population of China: a large-scale population-based cross-sectional study
Effect on preoperative anxiety of a personalized three-dimensional kidney model prior to nephron-sparing surgery for renal tumor: study protocol for a randomized controlled trial (Rein 3D Print-Anxiety – UroCCR 113)
Background The announcement of a diagnosis can be a source of anxiety for patients. Managing this anxiety is a major challenge, in terms of quality of life but also for the use of anxiolytic and analgesic therapies. The use of 3D modeling technology in partial nephrectomy surgery has proved its worth as a surgical aid but it could also help patients to manage their own care, by reducing their anxiety and increasing their understanding of the disease and its treatment. We aim to test this hypothesis with a prospective multicenter trial. Methods R3DP-A (Rein 3D – Anxiety) is an unblinded, multicenter, randomized, prospective, superiority-controlled trial. Participants are patients with kidney tumors treated by robot-assisted partial laparoscopic nephrectomy. The 234 patients (78x3 groups) from 6 French centers will undergo a pre-operative consultation dedicated to a personalized explanation of the surgical management and its risks. They will be randomized into three (1:1:1) groups corresponding to three types of support for consultation: use of a virtual 3D model of the kidney and its tumor; a printed 3D model; or the standard information sheet from the French Association of Urology (control group). Several self-questionnaires will be sent by the UroConnect® application and completed at different times during the study. The primary endpoint will be pre-operative anxiety (STAI-state questionnaire completed the day before surgery D-1). Secondary endpoints will be changes in anxiety levels between the pre-operative and post-operative consultations (between inclusion and D15 post-op), changes in health literacy and quality of life (HLSEU-Q16 and EQ-5D-5L questionnaires at inclusion and D15), feelings of understanding of the disease and its treatment at pre-operative period (Wake questionnaire at D-1), and consultation times. Discussion We aim to highlight a benefit of using a personalized 3D model on the anxiety level of patients undergoing partial nephrectomy surgery, as well as on their level of understanding of their pathology and its surgical treatment. The use of these models could be incorporated into current practice to improve patient experience throughout care.
The reconstruction method for static exterior model of digital twin railway station based on mobile vehicle
Aspirin prevents metastasis by limiting platelet TXA2 suppression of T cell immunity
Abstract Metastasis is the spread of cancer cells from primary tumours to distant organs and is the cause of 90% of cancer deaths globally1,2. Metastasizing cancer cells are uniquely vulnerable to immune attack, as they are initially deprived of the immunosuppressive microenvironment found within established tumours3. There is interest in therapeutically exploiting this immune vulnerability to prevent recurrence in patients with early cancer at risk of metastasis. Here we show that inhibitors of cyclooxygenase 1 (COX-1), including aspirin, enhance immunity to cancer metastasis by releasing T cells from suppression by platelet-derived thromboxane A2 (TXA2). TXA2 acts on T cells to trigger an immunosuppressive pathway that is dependent on the guanine exchange factor ARHGEF1, suppressing T cell receptor-driven kinase signalling, proliferation and effector functions. T cell-specific conditional deletion of Arhgef1 in mice increases T cell activation at the metastatic site, provoking immune-mediated rejection of lung and liver metastases. Consequently, restricting the availability of TXA2 using aspirin, selective COX-1 inhibitors or platelet-specific deletion of COX-1 reduces the rate of metastasis in a manner that is dependent on T cell-intrinsic expression of ARHGEF1 and signalling by TXA2 in vivo. These findings reveal a novel immunosuppressive pathway that limits T cell immunity to cancer metastasis, providing mechanistic insights into the anti-metastatic activity of aspirin and paving the way for more effective anti-metastatic immunotherapies.
Development of a functioning metric for the ageing population using data from the survey of health, ageing and retirement in Europe (SHARE)
Background Beyond mortality and morbidity, health statistics would benefit from reporting information on functioning, the third health indicator. The objective of this article is to use data from the Swiss Survey of Health, Ageing and Retirement in Europe (SHARE) to exemplarily create a psychometrically sound and valid metric of functioning for the ageing population living in Switzerland. Methods Partial Credit Model (PCM) analysis, including analysis of targeting, item fit, local item dependencies (LID), unidimensionality, and differential item functioning (DIF), tested the psychometric properties of selected items. The DIF analysis investigated the invariance of item difficulties across sex and age groups, country, language, and the assessment Wave. Results Data from 34,092 individuals aged 50 years and older was selected across assessment Waves of SHARE. The analysis showed that a functioning metric can be constructed with a total of 33 functioning items. Items showed LID and multidimensionality initially, which was solved with a testlet approach. Aggregation into testlets resulted in good fit, unidimensionality, no LID, and no DIF for sex, country, language, and the assessment Wave. Some DIF is found for age groups. The analysis also showed that the selected items target higher levels of problems in functioning than observed in the study population. Conclusions A functioning metric can be constructed from selected functioning items of SHARE. The metric provides a sound interval-scaled score that can be used for longitudinal analyses of ageing in Switzerland and neighboring countries or as an indicator of the level of functioning in an ageing population.
Computational analysis of zoanthamine alkaloids from Zoanthus sp. as potential DKK1 and GSK-3β inhibitors for osteoporosis therapy via Wnt signaling
Mystery of medieval manuscripts revealed by ancient DNA
Patent value prediction in biomedical textiles: A method based on a fusion of machine learning models
Patent value prediction is essential for technology innovation management. This study aims to enhance technology innovation management in the field of biomedical textiles by processing complex biomedical patent information to improve the accuracy of predicting patent values. A patent value grading prediction method based on a fusion of machine learning models is proposed, utilizing 113,428 biomedical textile patents as the research sample. The method combines BERT (Bidirectional Encoder Representations from Transformers) and a stacking strategy to classify and predict the value class of biomedical textile patents using both textual information and structured patent features. We implemented this method for patent value prediction in biomedical textiles, leading to the development of BioTexVal—the first dedicated patent value prediction model for this domain. BioTexVal’s innovation lies in employing a stacking strategy that integrates multiple machine learning models to enhance predictive accuracy while leveraging unstructured data during training. Results have shown that this approach significantly outperforms previous predictive methods. Validated on 113,428 biomedical textile patents spanning from 2003 to 2023, BioTexVal achieved an accuracy of 88.38%. This study uses average annual forward citations as an indicator for distinguishing patent value grades. The method may require adjustments based on data characteristics when applied to other research fields to ensure its effectiveness.