scholarly journals Carbapenems: A Short Review about their Current Status

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Syeda Warda Zahra ◽  
Keyword(s):  
Cost Effectiveness ◽  
State Of The Art ◽  
Short Review ◽  
Current Status ◽  
Clinical Use ◽  
Beta Lactams ◽  
Beta Lactamases ◽  
Current State ◽  
The Cost

In this review, we summarize the current “state of the art” of carbapenem antibiotics and their role in our antimicrobial armamentarium. Among the beta-lactams currently available, carbapenems are unique because they are relatively resistant to hydrolysis by most beta-lactamases. Herein, we described the cost effectiveness, safety, and advantages of carbapenems as compared to other antibiotics. We also highlight important features of the carbapenems that are presently in clinical use: imipenem-cilastatin, meropenem, ertapenem, doripenem, panipenem-betamipron, and biapenem. In closing, we emphasize some major challenges related to oral formulatuion of carbapenems and different strategies to overcome these challenges.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

scholarly journals Carbapenems: Past, Present, and Future

2011 ◽  
Vol 55 (11) ◽  
pp. 4943-4960 ◽  
Author(s):  
Krisztina M. Papp-Wallace ◽  
Andrea Endimiani ◽  
Magdalena A. Taracila ◽  
Robert A. Bonomo
Keyword(s):  
Antimicrobial Activity ◽  
State Of The Art ◽  
Parent Compound ◽  
Antimicrobial Properties ◽  
Value Added ◽  
Clinical Use ◽  
Continue Development ◽  
Penicillin Binding Proteins ◽  
Current State ◽  
Initial Discovery

ABSTRACTIn this review, we summarize the current “state of the art” of carbapenem antibiotics and their role in our antimicrobial armamentarium. Among the β-lactams currently available, carbapenems are unique because they are relatively resistant to hydrolysis by most β-lactamases, in some cases act as “slow substrates” or inhibitors of β-lactamases, and still target penicillin binding proteins. This “value-added feature” of inhibiting β-lactamases serves as a major rationale for expansion of this class of β-lactams. We describe the initial discovery and development of the carbapenem family of β-lactams. Of the early carbapenems evaluated, thienamycin demonstrated the greatest antimicrobial activity and became the parent compound for all subsequent carbapenems. To date, more than 80 compounds with mostly improved antimicrobial properties, compared to those of thienamycin, are described in the literature. We also highlight important features of the carbapenems that are presently in clinical use: imipenem-cilastatin, meropenem, ertapenem, doripenem, panipenem-betamipron, and biapenem. In closing, we emphasize some major challenges and urge the medicinal chemist to continue development of these versatile and potent compounds, as they have served us well for more than 3 decades.

scholarly journals Engineered Extracellular Vesicles: Tailored-Made Nanomaterials for Medical Applications

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1838
Author(s):  
Kenny Man ◽  
Mathieu Y. Brunet ◽  
Marie-Christine Jones ◽  
Sophie C. Cox
Keyword(s):  
Extracellular Vesicles ◽  
Tissue Regeneration ◽  
State Of The Art ◽  
Medical Applications ◽  
Clinical Use ◽  
Current State ◽  
Pathological Conditions ◽  
Potential Efficacy ◽  
Biomimetic Nanoparticles ◽  
Natural Cell

Extracellular vesicles (EVs) are emerging as promising nanoscale therapeutics due to their intrinsic role as mediators of intercellular communication, regulating tissue development and homeostasis. The low immunogenicity and natural cell-targeting capabilities of EVs has led to extensive research investigating their potential as novel acellular tools for tissue regeneration or for the diagnosis of pathological conditions. However, the clinical use of EVs has been hindered by issues with yield and heterogeneity. From the modification of parental cells and naturally-derived vesicles to the development of artificial biomimetic nanoparticles or the functionalisation of biomaterials, a multitude of techniques have been employed to augment EVs therapeutic efficacy. This review will explore various engineering strategies that could promote EVs scalability and therapeutic effectiveness beyond their native utility. Herein, we highlight the current state-of-the-art EV-engineering techniques with discussion of opportunities and obstacles for each. This is synthesised into a guide for selecting a suitable strategy to maximise the potential efficacy of EVs as nanoscale therapeutics.

Clinical use of cardiac PET/MRI: current state-of-the-art and potential future applications

2018 ◽  
Vol 36 (5) ◽  
pp. 313-323 ◽  
Author(s):  
Patrick Krumm ◽  
Stefanie Mangold ◽  
Sergios Gatidis ◽  
Konstantin Nikolaou ◽  
Felix Nensa ◽  
...  
Keyword(s):  
State Of The Art ◽  
Clinical Use ◽  
Cardiac Pet ◽  
Current State

Techniques of medical sciences

2012 ◽  
Keyword(s):  
Cost Effectiveness ◽  
Diagnostic Tests ◽  
Clinical Use ◽  
Medical Sciences ◽  
Test Sensitivity ◽  
Clinical Investigations ◽  
Clinical Scenarios ◽  
Test Specificity ◽  
Underlying Mechanisms ◽  
The Cost

There are a limited number of laboratory techniques that underlie the large number of clinical investigations that are used routinely. Knowing and understanding the basis for these tests is essential in appreciating the clinical application of the various tests. Important parameters of clinical diagnostic tests are test sensitivity—how well are those with a condition correctly identified by the test and how low is the rate of false positives; and test specificity—how well does the test correctly identify those without the condition and what is the rate of false negatives. The cost-effectiveness of a test is also an important consideration. Familiarity with the underlying mechanisms will also help students and doctors to determine when to use the tests, to realize their value and limitations, and hence to exercise caution in interpretation. This chapter has questions that test knowledge of the mechanisms underlying a variety of techniques. Their application in clinical use is tested using a number of clinical scenarios.

scholarly journals Cost-Based Goal Recognition in Navigational Domains

2019 ◽  
Vol 64 ◽  
pp. 197-242 ◽  
Author(s):  
Peta Masters ◽  
Sebastian Sardina
Keyword(s):  
State Of The Art ◽  
Identical Result ◽  
Task Planning ◽  
Maximum Probability ◽  
Current State ◽  
Current Location ◽  
Starting Point ◽  
Goal Recognition ◽  
The Cost ◽  
Start Location

Goal recognition is the problem of determining an agent's intent by observing her behaviour. Contemporary solutions for general task-planning relate the probability of a goal to the cost of reaching it. We adapt this approach to goal recognition in the strict context of path-planning. We show (1) that a simpler formula provides an identical result to current state-of-the-art in less than half the time under all but one set of conditions. Further, we prove (2) that the probability distribution based on this technique is independent of an agent's past behaviour and present a revised formula that achieves goal recognition by reference to the agent's starting point and current location only. Building on this, we demonstrate (3) that a Radius of Maximum Probability (i.e., the distance from a goal within which that goal is guaranteed to be the most probable) can be calculated from relative cost-distances between the candidate goals and a start location, without needing to calculate any actual probabilities. In this extended version of earlier work, we generalise our framework to the continuous domain and discuss our results, including the conditions under which our findings can be generalised back to goal recognition in general task-planning.

scholarly journals Ulixes: Facial Recognition Privacy with Adversarial Machine Learning

2021 ◽  
Vol 2022 (1) ◽  
pp. 148-165
Author(s):  
Thomas Cilloni ◽  
Wei Wang ◽  
Charles Walter ◽  
Charles Fleming
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Facial Recognition ◽  
Clustering Methods ◽  
Non Invasive ◽  
Current State ◽  
Social Media Platforms ◽  
Adversarial Examples ◽  
The Cost ◽  
Photo Management

Abstract Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.

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