Search Results (13)

Klebsiella pneumoniae is well recognized as a serious cause of infection in healthcare-associated settings and immunocompromised individuals; however, accumulating evidence from resource-limited nations documents an alarming rise in community-acquired K. pneumoniae infections, manifesting as bacteremia and pneumonia as well as neonatal sepsis. The emergence of hypervirulent and antibiotic-resistant K. pneumoniae strains threatens treatment options for clinicians. Effective vaccination strategies could represent a viable alternative that would both preempt the need for antibiotics to treat K. pneumoniae infections and reduce the burden of K. pneumoniae disease globally. There are currently no approved K. pneumoniae vaccines. We review the evidence for K. pneumoniae lipopolysaccharide (LPS) as a vaccine and immunotherapeutic target and discuss the role of antibodies specific for the core or O-antigen determinants within LPS in protection against Klebsiella spp. disease. We expand on the known role of the Klebsiella spp. capsule and O-antigen modifications in antibody surface accessibility to LPS as well as the in vitro and in vivo effector functions reported for LPS-specific antibodies. We summarize key hypotheses stemming from these studies, review the role of humoral immunity against K. pneumoniae O-antigen for protection, and identify areas requiring further research. Full article

In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in image classification tasks. Our method involves the integration of segmentation masks as reconstruction targets within the CapsNet architecture. This integration helps in better feature extraction by focusing on significant image parts while reducing the number of parameters required for accurate classification. C-CapsNet combines principles from Efficient-CapsNet and the original CapsNet, introducing several novel improvements such as the use of segmentation masks to reconstruct images and a number of tweaks to the routing algorithm, which enhance both classification accuracy and interoperability. We evaluated C-CapsNet using the Oxford-IIIT Pet and SIIM-ACR Pneumothorax datasets, achieving mean F1 scores of 93% and 67%, respectively. These results demonstrate a significant performance improvement over traditional CapsNet and CNN models. The method’s effectiveness is further highlighted by its ability to produce clear and interpretable segmentation masks, which can be used to validate the network’s focus during classification tasks. Our findings suggest that C-CapsNet not only improves the accuracy of CapsNets but also enhances their explainability, making them more suitable for real-world applications, particularly in medical imaging. Full article

Climate change (CC) has become a central global topic within the multiple branches of social disciplines. Natural Language Processing (NLP) plays a superior role since it has achieved marvelous accomplishments in various application scenarios. However, CC debates are ambiguous and complicated to interpret even for humans, especially when it comes to the aspect-oriented fine-grained level. Furthermore, the lack of large-scale effective labeled datasets is always a plight encountered in NLP. In this work, we propose a novel weak-supervised Hybrid Attention Masking Capsule Neural Network (HAMCap) for fine-grained CC debate analysis. Specifically, we use vectors with allocated different weights instead of scalars, and a hybrid attention mechanism is designed in order to better capture and represent information. By randomly masking with a Partial Context Mask (PCM) mechanism, we can better construct the internal relationship between the aspects and entities and easily obtain a large-scale generated dataset. Considering the uniqueness of linguistics, we propose a Reinforcement Learning-based Generator-Selector mechanism to automatically update and select data that are beneficial to model training. Empirical results indicate that our proposed ensemble model outperforms baselines on downstream tasks with a maximum of 50.08% on accuracy and 49.48% on F1 scores. Finally, we draw interpretable conclusions about the climate change debate, which is a widespread global concern. Full article

Masks cover most areas of the face, resulting in a serious loss of facial identity information; thus, how to alleviate or eliminate the negative impact of occlusion is a significant problem in the field of unconstrained face recognition. Inspired by the successful application of attention mechanisms and capsule networks in computer vision, we propose ECA-Inception-Resnet-Caps, which is a novel framework based on Inception-Resnet-v1 for learning discriminative face features in unconstrained mask-wearing conditions. Firstly, Squeeze-and-Excitation (SE) modules and Efficient Channel Attention (ECA) modules are applied to Inception-Resnet-v1 to increase the attention on unoccluded face areas, which is used to eliminate the negative impact of occlusion during feature extraction. Secondly, the effects of the two attention mechanisms on the different modules in Inception-Resnet-v1 are compared and analyzed, which is the foundation for further constructing the ECA-Inception-Resnet-Caps framework. Finally, ECA-Inception-Resnet-Caps is obtained by improving Inception-Resnet-v1 with capsule modules, which is explored to increase the interpretability and generalization of the model after reducing the negative impact of occlusion. The experimental results demonstrate that both attention mechanisms and the capsule network can effectively enhance the performance of Inception-Resnet-v1 for face recognition in occlusion tasks, with the ECA-Inception-Resnet-Caps model being the most effective, achieving an accuracy of 94.32%, which is 1.42% better than the baseline model. Full article

Iron deficiency is the principal cause of nutritional anemia and it constitutes a major health problem, especially during pregnancy. Despite the availability of various non-invasive traditional oral dosage forms such as tablets, capsules, and liquid preparations of iron, they are hard to consume for special populations such as pregnant women, pediatric, and geriatric patients with dysphagia and vomiting tendency. The objective of the present study was to develop and characterize pullulan-based iron-loaded orodispersible films (i-ODFs). Microparticles of iron were formulated by a microencapsulation technique, to mask the bitter taste of iron, and ODFs were fabricated by a modified solvent casting method. Morphological characteristics of the microparticles were identified by optical microscopy and the percentage of iron loading was evaluated by inductively coupled plasma optical emission spectroscopy (ICP-OES). The fabricated i-ODFs were evaluated for their morphology by scanning electron microscopy. Other parameters including thickness, folding endurance, tensile strength, weight variation, disintegration time, percentage moisture loss, surface pH, and in vivo animal safety were evaluated. Lastly, stability studies were carried out at a temperature of 25 °C/60% RH. The results of the study confirmed that pullulan-based i-ODFs had good physicochemical properties, excellent disintegration time, and optimal stability at specified storage conditions. Most importantly, the i-ODFs were free from irritation when administered to the tongue as confirmed by the hamster cheek pouch model and surface pH determination. Collectively, the present study suggests that the film-forming agent, pullulan, could be successfully employed on a lab scale to formulate orodispersible films of iron. In addition, i-ODFs can be processed easily on a large scale for commercial use. Full article

Multimodal sentiment analysis, which aims to recognize the emotions expressed in multimodal data, has attracted extensive attention in both academia and industry. However, most of the current studies on user-generated reviews classify the overall sentiments of reviews and hardly consider the aspects of user expression. In addition, user-generated reviews on social media are usually dominated by short texts expressing opinions, sometimes attached with images to complement or enhance the emotion. Based on this observation, we propose a visual enhancement capsule network (VECapsNet) based on multimodal fusion for the task of aspect-based sentiment analysis. Firstly, an adaptive mask memory capsule network is designed to extract the local clustering information from opinion text. Then, an aspect-guided visual attention mechanism is constructed to obtain the image information related to the aspect phrases. Finally, a multimodal fusion module based on interactive learning is presented for multimodal sentiment classification, which takes the aspect phrases as the query vectors to continuously capture the multimodal features correlated to the affective entities in multi-round iterative learning. Otherwise, due to the limited number of multimodal aspect-based sentiment review datasets at present, we build a large-scale multimodal aspect-based sentiment dataset of Chinese restaurant reviews, called MTCom. The extensive experiments both on the single-modal and multimodal datasets demonstrate that our model can better capture the local aspect-based sentiment features and is more applicable for general multimodal user reviews than existing methods. The experimental results verify the effectiveness of our proposed VECapsNet. Full article

A pharmaceutical vehicle based on lyophilized liposomes is proposed for the buccal administration of drugs aimed at systemic delivery through the sublingual mucosa. Liposomes made of egg phosphatidylcholine and cholesterol (7/3 molar ratio) were prepared and lyophilized in the presence of different additive mixtures with mucoadhesive and taste-masking properties. Palatability was assayed on healthy volunteers. The lyophilization cycle was optimized, and the lyophilized product was compressed to obtain round and capsule-shaped tables that were evaluated in healthy volunteers. Tablets were also assayed regarding weight and thickness uniformities, swelling index and liposome release. The results proved that lyophilized liposomes in unidirectional round tablets have palatability, small size, comfortability and buccal retention adequate for sublingual administration. In contact with water fluids, the tablets swelled, and rehydrated liposomes were released at a slower rate than permeation efficiency determined using a biomimetic membrane. Permeability efficiency values of 0.72 ± 0.34 µg/cm²/min and 4.18 ± 0.95 µg/cm²/min were obtained for the liposomes with and without additives, respectively. Altogether, the results point to the vehicle proposed as a liposomal formulation suitable for systemic drug delivery through the sublingual mucosa. Full article

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB). Patients & Methods: All consecutive outpatients with OGIB from a tertiary care center received a PEG-based preparation, without or with simethicone, in two different periods. The primary outcome was a difference in the proportions (%) of frames with abundant bubbles (>10%) along the full-length video sequences between the two periods. SB CE recordings were analyzed by a validated computed algorithm based on a grey-level of co-occurrence matrix (GLCM), to assess the abundance of bubbles in each frame. Results: In total, 105 third generation SB CE recordings were analyzed (48 without simethicone and 57 with simethicone-added preparations). A significant association was shown between the use of a simethicone-added preparation and a lower abundance of bubbles along the SB (p = 0.04). A significantly lower proportion of “abundant in bubbles” frames was observed in the fourth quartile (30.5% vs. 20.6%, p = 0.02). There was no significant impact of the use of simethicone in terms of diagnostic yield, SB transit time and completion rate. Conclusion: An accurate and reproducible computed algorithm demonstrated significant decrease in the abundance of bubbles along SB CE recordings, with a marked effect in the last quartile, in patients for whom simethicone had been added in PEG-based preparations, compared to those without simethicone. Full article

The aim of this study was to evaluate the impact of α-s1 casozepin on cat stress responses at a veterinary practice. Cats feel confident in their familiar surroundings and daily routine. A visit, and transport, to the veterinarian is a stressful experience for cats and their owners. Stress can mask clinical signs and has physiological impacts. Alpha-s1 casozepin (Zylkène; Vétoquinol) could potentially minimize stress in cats with its calming and anxiolytic characteristics. A randomized, partial double-blind and placebo-controlled study was carried out with 60 adult cats. The trial was designed for three groups: low dose (15 mg/kg q24 h α-s1 casozepin for six days), high dose (75 mg/kg q24 h α-s1 casozepin for three days), and a placebo (one fructose capsule per day for three days). For the study, cats had a checkup at their trusted veterinarian without the dietary supplement, followed by a second one four weeks later. Alpha-s1 casozepin or a placebo was administered three to six days before the checkup. Fecal cortisol metabolites (FCMs) were measured to non-invasively evaluate the impact of α-s1 casozepin on adrenocortical activity. The cat owners and veterinarians also assessed the physiological reactions (respiratory rate, sweaty paws, pupils, panting, and vocalization) of the cats at home, in the waiting area, and in the examination room. The only significant effect (kappa coefficient κ = 0.007 and κ = 0.003) found in this study was the absence of sweaty paws in cats who were treated with the high dose of α-s1 casozepin over three days, observed in the waiting area and examination room of the veterinarian’s practice, respectively. Alpha-s1 casozepin also showed a small but insignificant reduction in FCM levels. Alpha-s1 casozepin influences the autonomic nervous system, and can inhibit sweaty paws during stressful situations for cats. Full article

The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods. Full article

Cryptococcus species, the etiological agents of cryptococcosis, are encapsulated fungal yeasts that predominantly cause disease in immunocompromised individuals, and are responsible for 15% of AIDS-related deaths worldwide. Exposure follows the inhalation of the yeast into the lung alveoli, making it incumbent upon the pattern recognition receptors (PRRs) of pulmonary phagocytes to recognize highly conserved pathogen-associated molecular patterns (PAMPS) of fungi. The main challenges impeding the ability of pulmonary phagocytes to effectively recognize Cryptococcus include the presence of the yeast’s large polysaccharide capsule, as well as other cryptococcal virulence factors that mask fungal PAMPs and help Cryptococcus evade detection and subsequent activation of the immune system. This review will highlight key phagocyte cell populations and the arsenal of PRRs present on these cells, such as the Toll-like receptors (TLRs), C-type lectin receptors, NOD-like receptors (NLRs), and soluble receptors. Additionally, we will highlight critical cryptococcal PAMPs involved in the recognition of Cryptococcus. The question remains as to which PRR–ligand interaction is necessary for the recognition, phagocytosis, and subsequent killing of Cryptococcus. Full article

The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R² values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R² of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R² 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage. Full article

Pathogens reduce immune recognition of their cell surfaces using a variety of inert structural polysaccharides. For example, capsular polysaccharides play critical roles in microbial survival strategies. Capsules are widely distributed among bacterial species, but relatively rare in eukaryotic microorganisms, where they have evolved considerable complexity in structure and regulation and are exemplified by that of the HIV/AIDS-related fungus Cryptococcus neoformans. Endemic fungi that affect normal hosts such as Histoplasma capsulatum and Blastomyces dermatitidis have also evolved protective polysaccharide coverings in the form of immunologically inert α-(1,3)-glucan polysaccharides to protect their more immunogenic β-(1,3)-glucan-containing cell walls. In this review we provide a comparative update on bacterial and fungal capsular structures and immunogenic properties as well as the polysaccharide masking strategies of endemic fungal pathogens. Full article