CNS disorders are on the rise despite advancements in our understanding of their pathophysiological mechanisms. A major hurdle to the treatment of these disorders is the blood-brain barrier (BBB), ...which serves as an arduous janitor to protect the brain. Many drugs are being discovered for CNS disorders, which, however fail to enter the market because of their inability to cross the BBB. This is a pronounced challenge for the pharmaceutical fraternity. Hence, in addition to the discovery of novel entities and drug candidates, scientists are also developing new formulations of existing drugs for brain targeting. Several approaches have been investigated to allow therapeutics to cross the BBB. As the molecular structure of the BBB is better elucidated, several key approaches for brain targeting include physiological transport mechanisms such as adsorptive-mediated transcytosis, inhibition of active efflux pumps, receptor-mediated transport, cell-mediated endocytosis, and the use of peptide vectors. Drug-delivery approaches comprise delivery from microspheres, biodegradable wafers, and colloidal drug-carrier systems (e.g., liposomes, nanoparticles, nanogels, dendrimers, micelles, nanoemulsions, polymersomes, exosomes, and quantum dots). The current review discusses the latest advancements in these approaches, with a major focus on articles published in 2015 and 2016. In addition, we also cover the alternative delivery routes, such as intranasal and convection-enhanced diffusion methods, and disruption of the BBB for brain targeting.
Learning Deep Features for One-Class Classification Perera, Pramuditha; Patel, Vishal M.
IEEE transactions on image processing,
2019-Nov., 2019-Nov, 2019-11-00, 20191101, Letnik:
28, Številka:
11
Journal Article
Recenzirano
Odprti dostop
We present a novel deep-learning-based approach for one-class transfer learning in which labeled data from an unrelated task is used for feature learning in one-class classification. The proposed ...method operates on top of a convolutional neural network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss, are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection, and mobile active authentication datasets show that the proposed deep one-class (DOC) classification method achieves significant improvements over the state-of-the-art.
We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, ...HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.
•Survey on CNN-based approaches for crowd counting and density estimation.•Discussion on recent hand-crafted representations-based methods.•Recently datasets that pose various challenges are ...discussed.•Detailed analysis and comparison of results of CNN-based and traditional methods.•Discussion on future directions and trends for further progress.
Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. In this paper, we provide a comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations. First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets. Furthermore, we discuss the merits and drawbacks of existing CNN-based approaches and identify promising avenues of research in this rapidly evolving field.
Dental caries vaccine: are we there yet? Patel, M.
Letters in applied microbiology,
January 2020, 2020-Jan, 2020-01-01, 20200101, Letnik:
70, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Dental caries, caused by Streptococcus mutans, is a common infection. Caries vaccine has been under investigation for the last 40 years. Many in vitro and in vivo studies and some human clinical ...trials have determined many pertinent aspects regarding vaccine development. The virulence determinants of Strep. mutans, such as Ag I/II, responsible for adherence to surfaces, glucosyltransferase, responsible for the production of glucan, and the glucan‐binding protein, responsible for the attachment of glucan to surfaces, have been known to elicit an antigen‐specific immune response. It is also known that more than one antigen or a functional part of the genome responsible for these virulence determinants provide a better host response compared with the monogenic vaccine or complete genome of a specific antigen. To enhance the host response, the use of adjuvants has been studied and the routes of antigen administration have been investigated. In recent years, some promising vaccines such as pGJA‐P/VAX, LT derivative/Pi39‐512, KFD2‐rPAc and SBR/GBR‐CMV‐nirB have been developed and tested in animals. New virulence targets need to be explored. Multicentre collaborative studies and human clinical trials are required and some interest from funders and public health experts should be generated to overcome this hurdle.
Significance and Impact of the Study
Dental caries is an irreversible, multifactorial opportunistic infection. The treatment is costly, making it a public health problem. Despite many years of promising laboratory research, animal studies and clinical trials, there is no commercially available vaccine today. The research objectives have become more refined from lessons learnt over the years. Multigenic DNA/recombinant vaccines, using the best proved adjuvants with a delivery system for the nasal or sublingual route, should be developed and researched with multicentre collaborative efforts. In addition, new vaccine targets can be identified. To overcome the economic hurdle, funders and public health interest should be stimulated.
Significance and Impact of the Study: Dental caries is an irreversible, multifactorial opportunistic infection. The treatment is costly, making it a public health problem. Despite many years of promising laboratory research, animal studies and clinical trials, there is no commercially available vaccine today. The research objectives have become more refined from lessons learnt over the years. Multigenic DNA/recombinant vaccines, using the best proved adjuvants with a delivery system for the nasal or sublingual route, should be developed and researched with multicentre collaborative efforts. In addition, new vaccine targets can be identified. To overcome the economic hurdle, funders and public health interest should be stimulated.
Transcatheter aortic valve implantation (TAVI) is associated with a significant learning curve. There is paucity of data regarding the effect of hospital volume on outcomes after TAVI. This is a ...cross-sectional study based on Healthcare Cost and Utilization Project's Nationwide Inpatient Sample database of 2012. Subjects were identified by International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes, 35.05 (Trans-femoral/Trans-aortic Replacement of Aortic Valve) and 35.06 (Trans-apical Replacement of Aortic Valve). Annual hospital TAVI volumes were calculated using unique identification numbers and then divided into quartiles. Multivariate logistic regression models were created. The primary outcome was inhospital mortality; secondary outcome was a composite of inhospital mortality and periprocedural complications. Length of stay (LOS) and cost of hospitalization were assessed. The study included 1,481 TAVIs (weighted n = 7,405). Overall inhospital mortality rate was 5.1%, postprocedural complication rate was 43.4%, median LOS was 6 days, and median cost of hospitalization was $51,975. Inhospital mortality rates decreased with increasing hospital TAVI volume with a rate of 6.4% for lowest volume hospitals (first quartile), 5.9% (second quartile), 5.2% (third quartile), and 2.8% for the highest volume TAVI hospitals (fourth quartile). Complication rates were significantly higher in hospitals with the lowest volume quartile (48.5%) compared to hospitals in the second (44.2%), third (39.7%), and fourth (41.5%) quartiles (p <0.001). Increasing hospital volume was independently predictive of shorter LOS and lower hospitalization costs. In conclusion, higher annual hospital volumes are significantly predictive of reduced postprocedural mortality, complications, shorter LOS, and lower hospitalization costs after TAVI.
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