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Alginate is a polysaccharide that has found numerous applications in the domain of pharmaceutical science, paper and textile industry, food industry, dental applications, welding roads, mucoadhesive properties, scaffolding, biomedical and engineering due to its cost-effective nature, film forming ability, gelling, biocompatibility, biodegradability, nontoxic, non-immunogenic, readily availability, antimicrobial nature. Hydrogels of alginates play a crucial role in well-controlled or sustained release drug delivery, wound healing, and tissue engineering. This book chapter will provide a detailed overview of Alginate and its applications. To the best of our knowledge there no such type of informative data is available on Alginate and its relation with drug susceptibility. Here we more focused on the antibiotic capability of Alginate and its association with bacterial resistance.

Background Ablation of persistent atrial fibrillation (AF) remains challenging. Identification and ablation of localized AF drivers may offer the possibility for improved outcomes. Ripple map is a novel software algorithm that may allow improved localization of possible AF drivers through the whole chamber graphical display of continuously recorded bipolar electrograms. The objective of this study was to determine whether regions of high‐frequency Ripple activation (HFRA) observed on Ripple map provide useful ablation targets in patients with persistent AF. Methods and Results Consecutive patients underwent the first‐time ablation of persistent AF (n = 162) using a standard stepwise (n = 105) or a Ripple map guided approach (n = 57). Ripple map guided patients underwent pulmonary vein antral isolation followed by ablation of HFRA sites. Acute termination of AF was observed in 91.2% of the Ripple‐guided patients vs 52.4% in the stepwise approach, P

Background Atrial fibrillation ( AF ) patterns and their relations with long‐term prognosis are uncertain, partly because pattern definitions are challenging to implement in longitudinal data sets. We developed a novel AF classification algorithm and examined AF patterns and outcomes in the community. Methods and Results We characterized AF patterns between 1980 and 2005 among Framingham Heart Study participants who survived ≥1 year after diagnosis. We classified participants based on their pattern within the first 2 years after detection as having AF without recurrence, recurrent AF , or sustained AF . We examined associations between AF patterns and 10‐year survival using proportional hazards regression. Among 612 individuals with AF , mean age was 72.5±10.8 years, and 53% were men. Of these, 478 participants had ≥2 electrocardiograms (median, 3; limits 2 to 23) within 2 years after initial AF and were classified as having AF without 2‐year recurrence (n=63, 10%), recurrent AF (n=162, 26%) or sustained AF (n=207, 34%), although some (n=46, 8%) were indeterminate. Of 432 classified participants, 363 died, 75 had strokes, and 110 were diagnosed with heart failure during the next 10 years. Relative to individuals without AF recurrence, the multivariable‐adjusted mortality was higher among people with recurrent AF (hazard ratio [ HR ], 2.04; 95% confidence interval [ CI ], 1.26 to 3.29) and sustained AF ( HR , 2.36; 95% CI , 1.49 to 3.75). Conclusions In our community‐based AF sample, only 10% had AF without early‐term (2‐year) recurrence. Compared with individuals without 2‐year AF recurrences, the 10‐year prognosis was worse for individuals with either sustained or recurrent AF . Our proposed AF classification algorithm may be applicable in longitudinal datas ets.

CONTEXT: Although the heritability of atrial fibrillation (AF) is established, the contribution of familial AF to predicting new-onset AF remains unknown.OBJECTIVE: To determine whether familial occurrence of AF is associated with new-onset AF beyond established risk factors.DESIGN, SETTING, AND PARTICIPANTS: The Framingham Heart Study, a prospective community-based cohort study started in 1948. Original and Offspring Cohort participants were aged at least 30 years, were free of AF at the baseline examination, and had at least 1 parent or sibling enrolled in the study. The 4421 participants in this analysis (mean age, 54 [SD, 13] years; 54% women) were followed up through December 31, 2007.MAIN OUTCOME MEASURES: Incremental predictive value of incorporating different features of familial AF (any familial AF, premature familial AF [onset ≤65 years old], number of affected relatives, and youngest age of onset in a relative) into a risk model for new-onset AF.RESULTS: Across 11,971 examinations during the period 1968-2007, 440 participants developed AF. Familial AF occurred among 1185 participants (26.8%) and premature familial AF occurred among 351 participants (7.9%). Atrial fibrillation occurred more frequently among participants with familial AF than without familial AF (unadjusted absolute event rates of 5.8% and 3.1%, respectively). The association was not attenuated by adjustment for AF risk factors (multivariable-adjusted hazard ratio, 1.40; 95% confidence interval [CI], 1.13-1.74) or reported AF-related genetic variants. Among the different features of familial AF examined, premature familial AF was associated with improved discrimination beyond traditional risk factors to the greatest extent (traditional risk factors, C statistic, 0.842 [95% CI, 0.826-0.858]; premature familial AF, C statistic, 0.846 [95% CI, 0.831-0.862]; P = .004). Modest changes in integrated discrimination improvement were observed with premature familial AF (2.1%). Net reclassification improvement (assessed using 8-year risk thresholds of 10%) did not change significantly with premature familial AF (index statistic, 0.011; 95% CI, -0.021 to 0.042; P = .51), although categoryless net reclassification was improved (index statistic, 0.127; 95% CI, 0.064-0.189; P = .009).CONCLUSIONS: In this cohort, familial AF was associated with an increased risk of AF that was not attenuated by adjustment for AF risk factors including genetic variants. Assessment of premature familial AF was associated with a very slight increase in predictive accuracy compared with traditional risk factors.