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Experimental study on dynamic mechanical properties of hybrid fiber reinforced concrete at different temperatures
Climacteric women’s perspectives on menopause and hormone therapy: Knowledge gaps, fears, and the role of healthcare advice
Objective This study aims to evaluate the knowledge, attitudes, and practices of Brazilian women regarding menopause, related symptoms, and the use of hormone therapy, including indications and contraindications. Methods A cross-sectional study was conducted between 2022/07/18 and 2023/10/01, involving women aged 40–65 years from various cities in Minas Gerais, São Paulo, and other regions of Brazil. A structured KAP (Knowledge, Attitudes, and Practices) Survey was used to assess sociodemographic characteristics, the prevalence of menopausal symptoms, and the participants’ knowledge and practices concerning Menopause Hormone Therapy (MHT). Results The median age of the women surveyed was 50 years; 55.4% were postmenopausal (median age 55), and 44.6% were premenopausal (median age 45). The data indicate limited knowledge about menopause among Brazilian women. Less than one-third (30.3%) expressed satisfaction with the information they had received regarding menopause and available treatment options. Furthermore, 92.97% of participants demonstrated little or no knowledge of hormone therapy. Nearly 27% were unaware of MHT, and 22% opposed its use. Only 29% reported current use of MHT, with common reasons for discontinuation including fear of side effects and contraindications advised by gynecologists. Conclusion The findings indicate that many Brazilian women have insufficient knowledge about menopause and hormone therapy. Furthermore, a lack of information and training among healthcare providers may lead to the low utilization of menopausal hormone therapy (MHT). To effectively address menopausal symptoms and empower women to make informed choices about hormone therapy, it is crucial to improve access to accurate information and enhance the training of healthcare providers.
Metabolic mediators of the overweight’s effect on infertility in women with polycystic ovary syndrome
Distinct modes of interaction within eIF4F-like complexes and susceptibility to the RocA inhibitor for the Trypanosoma brucei EIF4AI translation initiation factor
Trypanosomatids are parasitic protozoa responsible for major human diseases which are characterized by unique gene expression mechanisms. mRNA translation in these parasites is associated with multiple eIF4F-like complexes, required for mRNA recruitment and ribosome binding. The eukaryotic eIF4F is generally known to require the action of eIF4A, an ATP-dependent RNA helicase, in order to function properly, but not all trypanosomatid eIF4F complexes might require EIF4AI, their single eIF4A homologue. In mammals, eIF4A is known to be targeted by specific inhibitors and can thus be considered a potential target for a selective inhibition of translation in these parasites. Here, aiming to better define the EIF4AI functionality, we started by investigating its interactome in Trypanosoma brucei, confirming a strong interaction with only one of five eIF4F-like complexes found in trypanosomatids, based on the EIF4E4/EIF4G3 subunits. Nevertheless, when the interactome of a mutant EIF4AI (DEAD/DQAD), known to be impacted on its ATPase activity, was investigated, the only eIF4F-like complex found was based on the EIF4E3/EIF4G4 pair, with many translation-related and other proteins also found with the mutant protein. When both wild-type and mutant proteins were also investigated through a fluorescent-based tethering assay, a stimulatory effect on mRNA expression was confirmed for EIF4AI, but not for the mutant protein. Sensitivity to the Rocaglamide A (RocA) inhibitor, which targets the mammalian eIF4A, was also investigated, with the inhibitor blocking the stimulation seen on the tethering assay. Parasite susceptibility to RocA was further assessed in T. brucei and Leishmania infantum, with both, and specially T. brucei, being much less susceptible to the drug than mammalian cells. This phenotype correlates with changes in EIF4AI within the RocA binding pocket where, in comparison with the mammalian eIF4A, a phenylalanine to valine substitution in the T. brucei EIF4AI likely impairs RocA binding. Our results help better define the EIF4AI mode of action in T. brucei and provide relevant data which might support future searches for specific EIF4AI inhibitors.
Mesoporous magnetic supported Cu complex for one-pot synthesis of 5-substituted 1H-tetrazoles in green media and the oxidation of sulfides
COVIVA: Effect of transcutaneous auricular vagal nerve stimulation on fatigue-syndrome in patients with Long Covid – A placebo-controlled pilot study protocol
Background: Up to 80% of patients who develop coronavirus disease-2019 (Covid-19) infection subsequently experience long covid/post-covid syndrome. The World Health Organization (WHO) has estimated that >770 million patients have been infected with Covid-19 globally. Even if only 10% of these patients develop long covid, > 75 million patients will suffer for a long period. Among the various symptoms of post-covid syndrome, fatigue is common, affecting up to 60% of the patients. As observed in other viral infections, elevated levels of inflammatory cytokines may play a role. Transcutaneous auricular vagal nerve stimulation (taVNS) is a noninvasive method that modulates the immune system via the central nervous system and has shown promising effects in autoimmune diseases and improving fatigue. In this pilot study, we investigated the feasibility of daily taVNS in patients with long covid-related fatigue. Additionally, the effects of taVNS on fatigue and quality of life will be analyzed. Methods: A total of 45 adult patients with long covid associated fatigue syndrome will be enrolled in this study, and will be randomized to the above-threshold-stimulation, below-threshold-stimulation, or sham-stimulation arms, after being informed that they will feel the stimulation. The above-threshold-group will receive a 4-week-long left-sided cymba conchae taVNS with 25 Hz, 250 µs pulse width 28s/32s on/off paradigm for 4 h throughout the day. The below-threshold group will receive stimulation below the sensational threshold, whereas the sham group will receive no stimulation following application of a non-functional electrode. The daily stimulation protocol will be recorded either manually or using the provided app. Three well-established questionnaires, the Multidimensional-Fatigue-Inventory-20, Short-Form-36, and Beck-Depression-Inventory, and the newly established Post-Covid-Syndrome-Score will be completed both before and after 4 weeks of stimulation. Discussion: The primary endpoint has been set as the patients’ average daily stimulation time after 4 weeks, while secondary endpoints include the effects of taVNS on fatigue and Quality of Live (QoL). As a non-invasive treatment option, taVNS may be a notable alternative for patients with post-covid related fatigue. Trial registration: This study was approved by the local ethics committee (23/7798) and registered (DRKS00031974) (see supporting information files). Ethics & Dissemination: The ethical justifiability of this study was supported by prior research demonstrating the safety of taVNS. Patients will be recruited by general practitioners, and written informed consent will be obtained. All data will be pseudonymized for collection and storage. The study results will be published in peer-reviewed journals with the aim of providing evidence of the potential of taVNS in long covid management. The study will be conducted in accordance with the principles of the Declaration of Helsinki.
Partial contrastive point cloud self-supervised representation learning
The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography
Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Few studies have investigated the impact of the feedback from such systems on the end user. This study investigated the impact of two forms of AI feedback (saliency/heatmaps and AI diagnosis with percentage confidence) on student and qualified diagnostic radiographers’ accuracy when determining binary diagnosis on skeletal radiographs. The AI feedback proved beneficial to accuracy in all cases except when the AI was incorrect and for pathological cases in the student group. The self-reported trust of all participants decreased from the beginning to the end of the study. The findings of this study should guide developers in the provision of the most advantageous forms of AI feedback and direct educators in tailoring education to highlight weaknesses in human interaction with AI-based clinical decision support systems.
Characterization of adverse reactions to four common targeted drugs for hepatocellular carcinoma in WHO-VigiAccess
A research on cross-age facial recognition technology based on AT-GAN
Currently, predicting a person’s facial appearance many years later based on early facial features remains a core technical challenge. In this paper, we propose a cross-age face prediction framework based on Generative Adversarial Networks (GANs). This framework extracts key features from early photos of the target individual and predicts their facial appearance at different ages in the future. Within our framework, we designed a GAN-based image restoration algorithm to enhance image deblurring capabilities and improve the generation of fine details, thereby increasing image resolution. Additionally, we introduced a semi-supervised learning algorithm called Multi-scale Feature Aggregation Scratch Repair (Semi-MSFA), which leverages both synthetic datasets and real historical photos to better adapt to the task of restoring old photographs. Furthermore, we developed a generative adversarial network incorporating a self-attention mechanism to predict age-progressed face images, ensuring the generated images maintain relatively stable personal characteristics across different ages. To validate the robustness and accuracy of our proposed framework, we conducted qualitative and quantitative analyses on open-source portrait databases and volunteer-provided data. Experimental results demonstrate that our framework achieves high prediction accuracy and strong generalization capabilities.
The association between triglyceride glucose-body mass index and mortality in critically ill patients with respiratory failure: insights from ICU data
Phase angle and water cell distribution as inflammation indicators linked to hematological markers across BMI categories
Effect of combined antimicrobial photodynamic therapy and photobiomodulation therapy in the management of recurrent herpes labialis: a randomized controlled trial
Tumor-associated long non-coding RNAs show variable expression across diffuse gliomas and effect on cell growth upon silencing in glioblastoma
Abstract Long noncoding RNAs (lncRNAs) have been recently recognized as critical components of cancer biology linked to oncogenic processes. Certain lncRNAs are known to act as oncogenes, and the disease-specific expression of many lncRNAs makes them informative biomarkers. We identified 22 uncharacterized lncRNAs from RNA-seq data of 169 glioblastoma (GBM) tumor samples sequenced by The Cancer Genome Atlas (TCGA) consortium and studied their expression in TCGA diffuse glioma cohort including also IDH-mutant astrocytomas and oligodendrogliomas as well as in normal brain samples from the Genotype-Tissue Expression cohort. All of the 22 lncRNAs were clearly upregulated in diffuse gliomas samples compared to the normal brain. Interestingly, 20 (91%) of these lncRNAs had significant expression differences between tumor grades and/or entities, and 14 (64%) were associated with overall patient survival. All 22 lncRNAs were expressed in at least one of the studied GBM cell lines and 10 (45%) were expressed in all four. When six of the lncRNAs were silenced in the SNB19 GBM cell line, the knock-down was associated with reduced growth and colony formation for three lncRNAs: TCONS_l2_00001282, lnc-GBMT-6, and lnc-NBN-1. In conclusion, the studied lncRNAs are associated with survival in patients with diffuse glioma and have functional relevance in GBM.
Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification
Abstract Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In this study, we explore the effectiveness of transfer learning in large-scale bird sound classification across various conditions, including single- and multi-label scenarios, and across different model architectures such as CNNs and Transformers. Our experiments demonstrate that both finetuning and knowledge distillation yield strong performance, with cross-distillation proving particularly effective in improving in-domain performance on Xeno-canto data. However, when generalizing to soundscapes, shallow finetuning exhibits superior performance compared to knowledge distillation, highlighting its robustness and constrained nature. Our study further investigates how to use multi-species labels, in cases where these are present but incomplete. We advocate for more comprehensive labeling practices within the animal sound community, including annotating background species and providing temporal details, to enhance the training of robust bird sound classifiers. These findings provide insights into the optimal reuse of pretrained models for advancing automatic bioacoustic recognition.
Lightweight faster R-CNN for object detection in optical remote sensing images
Abstract Various applications in remote sensing rely on object detection approaches, such as urban detection, precision farming, and disaster prediction. Faster RCNN has gained popularity for its performance but comes with significant computational and storage demands. Model compression techniques like pruning and quantization are frequently employed to mitigate these challenges. This paper introduces a novel bi-stage compression approach to create a lightweight Faster R-CNN for satellite images with minimal performance degradation. The proposed approach employs two distinct phases: aware training and post-training compression. First, aware training employs mixed-precision FP16 computation, which enhances training speed by a factor of 1.5 to 5.5 while preserving model accuracy and optimizing memory efficiency. Second, post-training compression applies unstructured weight pruning to eliminate redundant parameters, followed by dynamic quantization to reduce precision, thereby minimizing the model size at runtime and computational load. The proposed approach was assessed on the NWPU VHR-10 and Ship datasets. The results demonstrate an average 25.6% reduction in model size and a 56.6% reduction in parameters while maintaining the mean Average Precision (mAP).
In situ growth of luminescent d-f MOF nanostructures on bacterial cellulose as an accessible kit for early jaundice diagnosis
Research on the optimization of grouting flow detection sensor layouts based on laser ranging technology
Lane line detection based on cross-convolutional hybrid attention mechanism
Abstract In order to enhance the accuracy and robustness of lane line recognition in dynamic and complex environments, this paper proposes a lane line detection model based on a cross-convolutional hybrid attention mechanism (CCHA-Net). Unlike traditional approaches that separately employ channel and spatial attention, our proposed mechanism integrates these modalities through cross-convolution, thereby enabling cross-group feature interaction and dynamic spatial weight allocation. This novel integration not only improves the continuity of elongated lane features but also enhances the model’s ability to capture long-range dependencies in challenging scenarios. Additionally, this paper designs a lightweight message-passing module that employs dual-branch multi-scale convolutions to achieve cross-spatial domain feature fusion while reducing the number of parameters. Experimental results demonstrate that CCHA-Net achieves an F1 score of 80.2% on the CULane dataset and an accuracy of 96.8% on the TuSimple dataset, effectively enhancing lane line recognition accuracy in ever-changing and intricate environments.