Biofilm refers to the complex, sessile communities of microbes found either attached to a surface or buried firmly in an extracellular matrix as aggregates. The biofilm matrix surrounding bacteria makes them tolerant to harsh conditions and resistant to antibacterial treatments. Moreover, the biofilms are responsible for causing a broad range of chronic diseases and due to the emergence of antibiotic resistance in bacteria it has really become difficult to treat them with efficacy. Furthermore, the antibiotics available till date are ineffective for treating these biofilm related infections due to their higher values of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), which may result in in-vivo toxicity. Hence, it is critically important to design or screen anti-biofilm molecules that can effectively minimize and eradicate biofilm related infections. In the present article, we have highlighted the mechanism of biofilm formation with reference to different models and various methods used for biofilm detection. A major focus has been put on various anti-biofilm molecules discovered or tested till date which may include herbal active compounds, chelating agents, peptide antibiotics, lantibiotics and synthetic chemical compounds along with their structures, mechanism of action and their respective MICs, MBCs, minimum biofilm inhibitory concentrations (MBICs) as well as the half maximal inhibitory concentration (IC50) values available in the literature so far. Different mode of action of anti biofilm molecules addressed here are inhibition via interference in the quorum sensing pathways, adhesion mechanism, disruption of extracellular DNA, protein, lipopolysaccharides, exopolysaccharides and secondary messengers involved in various signaling pathways. From this study, we conclude that the molecules considered here might be used to treat biofilm-associated infections after significant structural modifications, thereby investigating its effective delivery in the host. It should also be ensured that minimum effective concentration of these molecules must be capable of eradicating biofilm infections with maximum potency without posing any adverse side effects on the host.

https://www.tandfonline.com/doi/pdf/10.1080/21505594.2017.1313372

Strategies for combating bacterial biofilms: A focus on anti-biofilm agents and their mechanisms of action

https://tandfonline.com/doi/pdf/10.1080/14686996.2017.1361305

Additive manufacturing of metals: a brief review of the characteristic microstructures and properties of steels, Ti-6Al-4V and high-entropy alloys

Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.

Bridge Centrality: A Network Approach to Understanding Comorbidity.

This study investigated the effect of economic growth on CO2 emission using the dynamic panel threshold framework. The analysis is based on data from a panel of 31 developing countries. The results...

https://www.econstor.eu/bitstream/10419/194721/1/1028902611.pdf

Effect of economic growth on CO2 emission in developing countries: Evidence from a dynamic panel threshold model

Introduction: There are 2 misconceptions about the cerebrospinal fluid (CSF), the blood-brain barrier (BBB), and brain drug delivery, which date back to the discovery of a barrier between blood and brain over 100 years ago. Misconception 1 is that drug distribution into CSF is a measure of BBB transport. Misconception 2 is that drug injected into the CSF compartment distributes to the inner parenchyma of brain.Areas Covered: Drug distribution into the CSF is a function of drug transport across the choroid plexus, which forms the blood-CSF barrier, and not drug transport across the BBB, which is situated at the microvascular endothelium of brain. Drug injected into CSF undergoes rapid efflux to the blood compartment via bulk flow. Drug penetration into brain parenchyma from the CSF is limited by diffusion and drug concentrations in brain decrease exponentially relative to the CSF concentration.Expert Opinion: The barrier between blood and brain was discovered in 1913, when it was believed that the ...

CSF, blood-brain barrier, and brain drug delivery

There has been a shift from the general presumption that “doctor knows best” to a heightened respect for patient autonomy. Medical ethics remains one-sided, however. It tends (incorrectly) to interpret patient autonomy as mere participation in decisions, rather than a willingness to take the consequences. In this respect, medical ethics remains largely paternalistic, requiring doctors to protect patients from the consequences of their decisions. This is reflected in a one-sided account of duties in medical ethics. Medical ethics may exempt patients from obligations because they are the weaker or more vulnerable party in the doctor-patient relationship. We argue that vulnerability does not exclude obligation. We also look at others ways in which patients’ responsibilities flow from general ethics: for instance, from responsibilities to others and to the self, from duties of citizens, and from the responsibilities of those who solicit advice. Finally, we argue that certain duties of patients counterbalance an otherwise unfair captivity of doctors as helpers.

Zhu Fengqing

Papers

Patients’ Responsibilities in Medical Ethics