Search Results (2,411)

Non-clinical enterococci are relatively poorly studied by means of acquired antibiotic resistance to tetracycline and by the distribution, functionality and role of their CRISPR systems. Background: In our study, 72 enterococcal strains, isolated from various non-clinical origins, were investigated for their phenotypic and genotypic (tet(M), tet(O), tet(S), tet(L), tet(K), tet(T) and tet(W)) tetracycline resistance. Methods: The genetic determinants for HGT (MGEs (Int-Tn and prgW), inducible pheromones (cpd, cop and cff), aggregation substances (agg, asa1, prgB and asa373) and CRISPR–Cas systems were characterized by PCR and whole-genome sequencing. Results: Four tet genes (tetM, tetO, tetS and tetT) were detected in 39% (n = 28) of our enterococcal population, with tetM (31%) being dominant. The gene location was linked to the Tn6009 transposon. All strains that contained tet genes also had genes for HGT. No tet genes were found in E. casseliflavus and E. gilvus. In our study, 79% of all tet-positive strains correlated with non-functional CRISPR systems. The strain E. faecalis BM15 was the only one containing a combination of a functional CRISPR system (cas1, cas2, csn2 and csn1/cas9) and tet genes. The CRISPR subtype repeats II-A, III-B, IV-A2 and VI-B1 were identified among E. faecalis strains (CM4-II-A, III-B and VI-B1; BM5-IV-A2, II-A and III-B; BM12 and BM15-II-A). The subtype II-A was the most present. These repeats enclosed a great number of spacers (1–10 spacers) with lengths of 31 to 36 bp. One CRISPR locus was identified in plasmid (p.Firmicutes1 in strain E. faecalis BM5). We described the presence of CRISPR loci in the species E. pseudoavium, E. pallens and E. devriesei and their lack in E. gilvus, E. malodoratus and E. mundtii. Conclusions: Our findings generally describe the acquisition of foreign DNA as a consequence of CRISPR inactivation, and self-targeting spacers as the main cause. Full article

Model lightweighting is significant in edge computing and mobile devices. Current studies on fast network design mainly focuses on model computation compression and speedup. Many models aim to compress models by dealing with redundant feature maps. However, most of these methods choose to preserve the feature maps with simple manipulations and do not effectively reduce redundant feature maps. This paper proposes a new convolution module, PDConv, which compresses redundant feature maps to reduce network complexity and increase network width to maintain accuracy. PDConv (Partial Deep Convolution) outperforms traditional methods in handling redundant feature maps, particularly in deep networks. Its FLOPs are comparable to deep separable convolution but with higher accuracy. This paper proposes PDBottleNeck and PDC2f (Partial Deep CSPDarknet53 to 2-Stage FPN) and build the lightweight network PDNet for experimental validation using the PASCAL VOC dataset. Compared to the popular HorNet, our method achieves an improvement of more than 25% in FLOPs and 1.8% in mAP50:95 accuracy. On the CoCo2017 dataset, our large PDNet achieves a 0.5% improvement in mAP75 and lower FLOPs than the latest RepVit. Full article

Vitamin D receptor [VDR] expression promotes LL37 expression, possibly contributing to host defense. The hypothesis was that an increase in 25 hydroxyvitamin D [25vitD] could lead to enhanced VDR expression and increased LL-37 production, thereby contributing to improved prognosis in critically ill patients. Methods: A nonblinded, randomized controlled trial was conducted. A total of 207 patients admitted to ICU with severe SARS-CoV-2 pneumonia were included and received different doses of cholecalciferol (500 MU, 3 MU/day, no cholecalciferol) during their ICU and hospital stay. 25vitD levels as well as LL37 and monocytes’ VDR gene expression were evaluated on admission and after. Clinical evolution, ICU mortality, hospital mortality, and 60-day mortality were evaluated. Results: The median age was 57.7 years and the majority of patients were Caucasian [87.4%] and male [70.5%]. There was a significant difference in 25vitD levels between groups on the third [p = 0.002] and seventh [p < 0.001] days. Patients supplemented with 500 MU of cholecalciferol had a very significant increase in monocytes’ VDR gene expression and showed a better clinical evolution in the ICU, with a significant correlation to evolution factors. Higher LL37 on admission had a significant negative association with hospital and ICU mortality, lost after adjustment for comorbidities to a nearly significant association with ICU, hospital, and 60-day mortality. Conclusion: Supplementation with higher doses of cholecalciferol may contribute to a significant increase in 25vitD levels but not in LL37 levels. Higher LL37 levels on admission may be related to a decrease in ICU, hospital, and 60-day mortality. VDR gene expression in monocytes is much higher in patients supplemented with higher doses of cholecalciferol. Full article

Vitamin E (VitE), a potent antioxidant, has demonstrated significant potential in mitigating oxidative stress and cellular damage, making it a valuable agent for countering environmental toxicities, including those caused by polystyrene nanoplastics (PSNPs). This study examined the effects of PSNPs on the zebrafish visual system and evaluated the protective role of VitE. Zebrafish embryos were exposed to PSNPs (0.01, 0.1, 1, and 10 μg/mL) with or without 20 μM VitE co-treatment from fertilization to 6 days post-fertilization (dpf). Visual function, morphology, and molecular responses were assessed at 4 or 6 dpf. Exposure to PSNPs at concentrations of 0.1 to 10 μg/mL significantly increased bioaccumulation in the zebrafish eye in a concentration-dependent manner and disrupted the visual system. These disruptions caused a reduction in the eye-to-body length ratio and decreased optomotor response positivity and swimming distance, indicating impaired visual function and behavior. Furthermore, PSNPs elevated reactive oxygen species (ROS) levels, induced retinal apoptosis, and disrupted gene expression related to visual development (six6, pax2, pax6a, and pax6b), apoptosis (tp53, casp3, bax, and bcl2a), and antioxidant defense (sod1, cat, and gpx1a). VitE co-treatment significantly mitigated these adverse effects, reducing oxidative damage, restoring antioxidant defenses, and preserving retinal function. This study highlights the potential of VitE as a protective agent against PSNP-induced visual dysfunction and underlines the urgent need to address nanoplastic pollution to protect aquatic ecosystems. Full article

Background: Cardiogenic shock (CS), characterized by inadequate tissue perfusion due to cardiac dysfunction, has a high mortality rate despite advances in treatment. Systemic inflammation and organ failure exacerbate the severity of CS. Extracorporeal hemadsorption techniques such as CytoSorb^® have been introduced to control inflammation. However, evidence of their efficacy, particularly in patients on various mechanical circulatory support (MCS) systems, remains limited. Methods: This retrospective study analyzed data from 129 CS patients treated with CytoSorb^® at the University Hospital of Marburg between August 2019 and December 2023. Those patients receiving MCS were grouped according to MCS type: (1) Impella, (2) VA-ECMO, and (3) ECMELLA. The hemodynamic parameters of circulatory support (e.g., MCS flow rates and vasoactive inotropic score, VIS) and laboratory and ventilation parameters were assessed 24 h before start of CytoSorb^® therapy (T1) and 24 h after completion of CytoSorb^® therapy (T2). Results: Of 129 CS patients (mean age: 64.7 ± 13.1 years), 103 (79.8%) received MCS. Comparing T1 and T2, there was a significant reduction in VIS in the entire cohort (T1: 38.0, T2: 16.3; p = 0.002), with a concomitant significant reduction in the level of MCS support in all subgroups, indicating successful weaning. Analysis of laboratory parameters showed significant reductions in lactate (T1: 2.1, T2: 1.3 mmol/L; p = 0.014), myoglobin (T1: 1549.0, T2: 618.0 µg/L; p < 0.01), lactate dehydrogenase (T1: 872.0, T2: 632.0 U/L; p = 0.048), and procalcitonin (T1: 2.9, T2: 1.6 µg/L; p < 0.001). However, a significant decrease in platelets (T1: 140.0, T2: 54.0 tsd/µL; p < 0.001) and albumin (T1: 25.0, T2: 22.0 g/dL; p < 0.001) was also documented. The median SOFA score of the entire cohort was 15.0 (IQR 12.0–16.0), predicting a mortality rate of >80%, which could be reduced to 60.5% in the present study. Conclusions: During CytoSorb^® therapy in CS, a significant reduction in VIS was demonstrated, resulting in improved organ perfusion. Therefore, the results of this study underline that CytoSorb^® therapy can be considered a useful “component” in the complex management of CS, especially when combined with MCS. To refine and optimize treatment strategies in CS, prospective studies are needed to better define the role of hemadsorption. Full article

Brugada syndrome (BrS) is an inherited electrical cardiac disorder that is associated with a higher risk of ventricular fibrillation (VF) and sudden cardiac death (SCD) in patients without structural heart disease. The diagnosis is based on the documentation of the typical pattern in the electrocardiogram (ECG) characterized by a J-point elevation of ≥2 mm, coved-type ST-segment elevation, and negative T wave in one or more right precordial leads, called type 1 Brugada ECG. Risk stratification is particularly difficult in asymptomatic cases. Patients who have experienced documented VF are generally recommended to receive an implantable cardioverter defibrillator to lower the likelihood of sudden death due to recurrent episodes. However, for asymptomatic individuals, the most appropriate course of action remains uncertain. Accurate risk prediction is critical to avoiding premature deaths and unnecessary treatments. Due to the challenges associated with experimental research on human cardiac tissue, alternative techniques such as computational modeling and deep learning-based artificial intelligence (AI) are becoming increasingly important. This study introduces a vision transformer (ViT) model that leverages 12-lead ECG images to predict potentially fatal arrhythmic events in BrS patients. This dataset includes a total of 278 ECGs, belonging to 210 patients which have been diagnosed with Brugada syndrome, and it is split into two classes: event and no event. The event class contains 94 ECGs of patients with documented ventricular tachycardia, ventricular fibrillation, or sudden cardiac death, while the no event class is composed of 184 ECGs used as the control group. At first, the ViT is trained on a balanced dataset, achieving satisfactory results (89% accuracy, 94% specificity, 84% sensitivity, and 89% F1-score). Then, the discarded no event ECGs are attached to additional 30 event ECGs, extracted by a 24 h recording of a singular individual, composing a new test set. Finally, the use of an optimized classification threshold improves the predictions on an unbalanced set of data (74% accuracy, 95% negative predictive value, and 90% sensitivity), suggesting that the ECG signal can reveal key information for the risk stratification of patients with Brugada syndrome. Full article

Background: To progress research into the biological mechanisms that link mammographic breast density to breast cancer risk, fibroglandular breast density can be used as a surrogate measure. This study aimed to develop a computational tool to classify fibroglandular breast density in hematoxylin and eosin (H&E)-stained breast tissue sections using deep learning approaches that would assist future mammographic density research. Methods: Four different architectural configurations of transferred MobileNet-v2 convolutional neural networks (CNNs) and four different models of vision transformers were developed and trained on a database of H&E-stained normal human breast tissue sections (965 tissue blocks from 93 patients) that had been manually classified into one of five fibroglandular density classes, with class 1 being very low fibroglandular density and class 5 being very high fibroglandular density. Results: The MobileNet-Arc 1 and ViT model 1 achieved the highest overall F1 scores of 0.93 and 0.94, respectively. Both models exhibited the lowest false positive rate and highest true positive rate in class 5, while the most challenging classification was class 3, where images from classes 2 and 4 were mistakenly classified as class 3. The area under the curves (AUCs) for all classes were higher than 0.98. Conclusions: Both the ViT and MobileNet models showed promising performance in the accurate classification of H&E-stained tissue sections across all five fibroglandular density classes, providing a rapid and easy-to-use computational tool for breast density analysis. Full article

An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately identify stem keypoints on embedded devices. YOLO-PP employs a C2FET module with an EfficientViT branch, utilizing parallel dual-path feature extraction to enhance detection performance in dense scenes. Additionally, we designed and implemented a Spatial Pyramid Squeeze Pooling (SPSP) module to extract fine features and capture multi-scale spatial information. Furthermore, a new loss function based on Inner-CIoU was developed specifically for keypoint tasks to further improve detection efficiency.The model was tested on a real greenhouse cherry tomato dataset, achieving an accuracy of 95.81%, a recall rate of 98.86%, and mean Average Precision (mAP) scores of 99.18% and 98.87% for mAP50 and mAP50-95, respectively. Compared to the DEKR, YOLO-Pose, and YOLOv8-Pose models, the mAP value of the YOLO-PP model improved by 16.94%, 10.83%, and 0.81%, respectively. The proposed algorithm has been implemented on NVIDIA Jetson edge computing devices, equipped with a human–computer interaction interface. The results demonstrate that the proposed Improved Picking Point Detection Network exhibits excellent performance and achieves real-time accurate detection of cherry tomato harvesting tasks in facility agriculture. Full article

This study investigates the fabrication and characterization of heat-responsive PLA/PU/MXene shape memory polymer blend nanocomposites with varying PLA content (10, 20, 30, and 50%) and a fixed MXene content of 0.5 wt.%. The results indicate significant improvements in mechanical properties, with the 50% PLA/PU/MXene blend showing a 300% increase in ultimate tensile strength and a 90% decrease in % elongation compared to pure PU. Additionally, the 50% blend exhibited a 400% increase in flexural strength. Microstructural analysis revealed dispersed pores and sea–island morphology in pure PU and the 50% PLA/PU/MXene blend. Thermal analysis using DSC showed an increase in crystallinity from 33% (pure PU) to 45% for the 50% PLA/PU/MXene blend, indicating enhanced crystalline domains due to the semi-crystalline nature of PLA and MXene’s influence on molecular ordering. TGA demonstrated a significant improvement in thermal stability, with the onset temperature rising from 185 °C (pure PU) to 212 °C and the degradation temperature increasing from 370 °C to 425 °C for the 50% blend, attributed to the rigid structure of PLA and MXene’s stabilizing effect. Shape memory testing revealed that the 30% PLA/PU/MXene blend achieved the best shape fixity and recovery with optimal performance, whereas higher PLA content diminished shape memory behavior. Full article

Background: Breast cancer screening through mammography interpretation is crucial for early detection and improved patient outcomes. However, the manual classification of mammograms using the BIRADS (Breast Imaging-Reporting and Data System) remains challenging due to subtle imaging features, inter-reader variability, and increasing radiologist workload. Traditional computer-aided detection systems often struggle with complex feature extraction and contextual understanding of mammographic abnormalities. To address these limitations, this study proposes MammoViT, a novel hybrid deep learning framework that leverages both ResNet50’s hierarchical feature extraction capabilities and Vision Transformer’s ability to capture long-range dependencies in images. Methods: We implemented a multi-stage approach utilizing a pre-trained ResNet50 model for initial feature extraction from mammogram images. To address the significant class imbalance in our four-class BIRADS dataset, we applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority classes. The extracted feature arrays were transformed into non-overlapping patches with positional encodings for Vision Transformer processing. The Vision Transformer employs multi-head self-attention mechanisms to capture both local and global relationships between image patches, with each attention head learning different aspects of spatial dependencies. The model was optimized using Keras Tuner and trained using 5-fold cross-validation with early stopping to prevent overfitting. Results: MammoViT achieved 97.4% accuracy in classifying mammogram images across different BIRADS categories. The model’s effectiveness was validated through comprehensive evaluation metrics, including a classification report, confusion matrix, probability distribution, and comparison with existing studies. Conclusions: MammoViT effectively combines ResNet50 and Vision Transformer architectures while addressing the challenge of imbalanced medical imaging datasets. The high accuracy and robust performance demonstrate its potential as a reliable tool for supporting clinical decision-making in breast cancer screening. Full article

Background/Objectives: Omeprazole undergoes degradation in acidic conditions, which makes it unstable in low pHs found in the gastric environment. The vast majority of already marketed omeprazole formulations use enteric polymer coatings to protect the drug from exposure to acidic pH in the stomach, allowing for drug release in the small intestine where the pH is higher. This study aimed to explore the technical aspects of using stomach acid neutralizers as an alternative to polymeric coatings for omeprazole. Methods: After evaluating various neutralizers, magnesium oxide and sodium bicarbonate were chosen to be incorporated into capsules containing omeprazole, which then underwent in vitro dissolution testing to assess their ability to maintain optimal pH levels and ensure appropriate dissolution kinetics. Hygroscopicity and chemical stability of the selected formulation were tested to prove pharmaceutical quality of the product. An in vivo pharmacokinetic study was conducted to demonstrate the efficacy of the omeprazole–sodium bicarbonate formulation in providing faster absorption in humans. Results: Sodium bicarbonate was selected as the most suitable antacid for ensuring omeprazole stabilization. Its quantity was optimized to effectively neutralize stomach acid, facilitating the rapid release and absorption of omeprazole. In vitro studies demonstrated the ability of the formulation to neutralize gastric acid within five minutes. In vivo studies indicated that maximum concentrations of omeprazole were achieved within half an hour. The product met the requirements of pharmaceutical quality. Conclusions: An easily manufacturable, fast-absorbing oral formulation was developed as an alternative to enteric-coated omeprazole. Full article

Glioblastoma, an aggressive cancer, is difficult to treat due to its location, late detection, drug resistance, and poor absorption of chemotherapeutics. Intratumoral drug administration offers a promising potential treatment alternative with localized delivery and minimal systemic toxicity. Vanadium(V) coordination complexes, incorporating Schiff base and catecholate ligands, have shown effects as antiproliferative agents with tunable efficacy and reactivity, stability, steric bulk, hydrophobicity, uptake, and toxicity optimized for the intratumoral administration vehicle. A new series of oxovanadium(V) Schiff base–catecholate complexes were synthesized and characterized using nuclear magnetic resonance (NMR), UV-Vis, and infrared spectroscopy and mass spectrometry. Stability under physiological conditions was assessed via UV-Vis spectroscopy, and the antiproliferative activity was evaluated in T98G glioblastoma and SVG p12 normal glial cells using viability assays. The newly synthesized [VO(3-tBuHSHED)(TIPCAT)] complex was more stable (t_1/2 ~ 4.5 h) and had strong antiproliferative activity (IC₅₀ ~ 1.5 µM), comparing favorably with the current lead compound, [VO(HSHED)(DTB)]. The structural modifications enhanced stability, hydrophobicity, and steric bulk through substitution with iso-propyl and tert-butyl groups. The improved properties were attributed to steric hindrance associated with the new Schiff base and catecholato ligands, as well as the formation of non-toxic byproducts upon degradation. The [VO(3-tBuHSHED)(TIPCAT)] complex emerges as a promising candidate for glioblastoma therapy by demonstrating enhanced stability and a greater selectivity, which highlights the role of strategic ligand design in developing localized therapies for the treatment of resistant cancers. In reporting the new class of compounds effective against T98G glioblastoma cells, we describe the generally desirable properties that potential drugs being developed for intratumoral administration should have. Full article

The rapid advancement of industrial automation in the automotive manufacturing sector has heightened demand for welding quality, particularly in critical component welding, where traditional manual inspection methods are inefficient and prone to human error, leading to low defect recognition rates that fail to meet modern manufacturing standards. To address these challenges, an enhanced YOLOv8-based algorithm for steel defect detection, termed YOLOv8-WD (weld detection), was developed to improve accuracy and efficiency in identifying defects in steel. We implemented a novel data augmentation strategy with various image transformation techniques to enhance the model’s generalization across different welding scenarios. The Efficient Vision Transformer (EfficientViT) architecture was adopted to optimize feature representation and contextual understanding, improving detection accuracy. Additionally, we integrated the Convolution and Attention Fusion Module (CAFM) to effectively combine local and global features, enhancing the model’s ability to capture diverse feature scales. Dynamic convolution (DyConv) techniques were also employed to generate convolutional kernels based on input images, increasing model flexibility and efficiency. Through comprehensive optimization and tuning, our research achieved a mean average precision (map) at IoU 0.5 of 90.5% across multiple datasets, contributing to improved weld defect detection and offering a reliable automated inspection solution for the industry. Full article

Developing AI algorithms for the game of Go has long been a challenging task. While tools such as AlphaGo have revolutionized gameplay, their focus on maximizing win rates often leads to moves that are incomprehensible to human players, limiting their utility as training aids. This work introduces a novel approach to bridge this gap by leveraging a Vision Transformer (ViT) to develop an AI model that achieves professional-level play while mimicking human decision-making. Using a dataset from the KGS Go server, our ViT-based model achieves 51.49% accuracy in predicting expert moves with a simple feature set. Comparative analysis against CNN-based models highlights the ViT’s superior performance in capturing patterns and replicating expert strategies. These findings establish ViTs as promising tools for enhancing Go training by aligning AI strategies with human intuition. Full article

The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced pre-trained large language models, which offer superior generative and multimodal understanding capabilities. Using a question-answering approach, we extract textual descriptions and category prediction probabilities for disaster scenarios, which are then used as input to our proposed Mamba Multimodal Disaster Recognition Network (Mamba-MDRNet). This model integrates a large pre-trained model with the Mamba mechanism, enabling the selection of the most reliable modality information as a robust basis for scene classification. Extensive experiments demonstrate consistent performance improvements across various visual models with heterogeneous architectures. Notably, integrating EfficientNet within Mamba-MDRNet yielded 97.82% accuracy for natural scene classification, surpassing the performance of the CNN (91.75%), ViT (94.50%), and ResNet18 (97.25%). These results highlight the potential of multimodal models combining large models and the Mamba mechanism for disaster type prediction. Full article